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main
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live-detec
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| 7465ef13ea | |||
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| bb5ce203f6 |
3
scripts/.gitignore
vendored
3
scripts/.gitignore
vendored
@ -1,2 +1,3 @@
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*.wav
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__pycache__/
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__pycache__/
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junk/
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63
scripts/README.md
Normal file
63
scripts/README.md
Normal file
@ -0,0 +1,63 @@
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# Planned algorithm
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From selcald:
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One possible solution to this problem is to step back from trying to determine the frequencies of the individual tones and to instead verify that:
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- There are two tones present
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- The difference in frequency between the two tones matches a pair of known tones in the alphabet
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The author of selcald seems awfully hung up on trying to ensure correct detection of the silent period
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between two tones. I don't think this is worth actively trying to detect, as it is more important
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to find two dominant tones for a period of ~1 sec, followed by another 1 sec with two dominant tones.
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The silence period is irrelevant - according to the spec of 0.2 +/- 0.1 sec _at the transmitter_ it
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could be impossibly short to reliably distinguish between successive `A*-A*` tones at the receiver.
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## Terrible pseudocode
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```
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collect samples from audio source <wavfile, arecord>
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low pass & decimate if necessary
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<simultaneously>
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run wide fft over a large sample window
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detect peak bin (with some hysteresis)
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if peak bin within tone band:
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assume difference is due to doppler shift
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gradually adjust expected freq(s) for tone(s) to match
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record RMS energy into buffer
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<simultaneously>
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run correlation over small sample window
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For each tone in the alphabet:
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Cross correlate the audio with the adjusted tone
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Record normalized tone energy into tone buffer
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For tone record in tone buffer:
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sort tones by amplitude
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if:
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- the two highest amplitude tones are within 3 dB of each other
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- the two highest amplitude tones are at least 3 dB greater than the next nearest tone
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- the sum of the two highest tones account for at least 60% of the modulation energy (-2.22 dB)
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(as determined relative to stable RMS energy calc'd by wide fft)
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(nominally 90% / -0.458 dB)
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then:
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record dominant two tones in tone buffer
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if any two tones are dominant tones for the majority of previous 1 sec:
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record dominant two tones in code buffer
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reset tone detection counter (prohibit detecting codes for another 0.5 sec)
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upon code record in code buffer:
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if dominant code previously occured within 1.25 sec
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emit SELCAL detection
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reset code detection counter (prohibit detecting codes for another 0.5 sec)
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```
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## Usage
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Either
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```
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./audio-capture.sh | ./live.py
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```
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or
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```
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./audio-pipe.sh ./samples/MA-ZC.wav | ./live.py
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```
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8
scripts/audio-capture.sh
Executable file
8
scripts/audio-capture.sh
Executable file
@ -0,0 +1,8 @@
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#! /bin/sh
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set -eux
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sample_rate="44100"
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channels="1"
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arecord -t raw -c ${channels} -f S16_LE -r ${sample_rate}
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9
scripts/audio-pipe.sh
Executable file
9
scripts/audio-pipe.sh
Executable file
@ -0,0 +1,9 @@
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#! /bin/sh
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set -eux
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audio_file=$1
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sample_rate="44100"
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channels="1"
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ffmpeg -hide_banner -loglevel error -i ${audio_file} -f s16le -ac ${channels} -ar ${sample_rate} -
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@ -1,20 +1,40 @@
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import numpy as np
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from scipy.signal import butter, lfilter, decimate
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def anti_alias(data, sample_rate, max_frequency):
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def anti_alias(data, sample_rate, max_frequency, order=5):
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FILTER_HEADROOM = 1.2
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nyquist_rate = 2 * max_frequency
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downsample_factor = 1
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if sample_rate > nyquist_rate:
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filtered_data = lowpass_filter(data, max_frequency, sample_rate)
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filtered_data = lowpass_filter(
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data,
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max_frequency * FILTER_HEADROOM,
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sample_rate,
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order
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)
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downsample_factor = int(sample_rate / nyquist_rate)
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filtered_data = decimate(filtered_data, downsample_factor)
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sample_rate = sample_rate // downsample_factor
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else:
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filtered_data = data
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return filtered_data, sample_rate
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return filtered_data, sample_rate, downsample_factor
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def smoothing_filter(data, window_size=256, mode='same'):
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window = np.ones(window_size) / window_size
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return np.convolve(data, window, mode=mode)
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# Stolen from selcald
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def note(freq, length, amp=1.0, rate=44100):
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if freq == 0:
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data = np.zeros(int(length * rate))
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else:
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t = np.linspace(0, length, int(length * rate))
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data = np.sin(2 * np.pi * freq * t) * amp
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return data
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# These originally came from https://scipy.github.io/old-wiki/pages/Cookbook/ButterworthBandpass,
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# but they've been copied around the internet so many times that ChatGPT now produces them verbatim.
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153
scripts/live.py
Executable file
153
scripts/live.py
Executable file
@ -0,0 +1,153 @@
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#! /usr/bin/env python3
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import sys
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import threading
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import numpy as np
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import signal as sig
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from tones import TONES
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from filters import anti_alias, bandpass_filter, note, smoothing_filter
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from scipy import signal
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from scipy.signal import butter, lfilter, decimate
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import pyqtgraph as pg
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from pyqtgraph.Qt import QtGui, QtWidgets, mkQApp
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import time
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keep_running = True
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def signal_handler(sig, frame):
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global keep_running
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print('SIGINT received. Stopping...')
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keep_running = False
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class DetectorGui(QtWidgets.QMainWindow):
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def __init__(self, *args, **kwargs):
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super(DetectorGui, self).__init__(*args, **kwargs)
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layout = pg.GraphicsLayoutWidget(show=True)
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self.setCentralWidget(layout)
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self.setWindowTitle('SELCAL Detector')
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self.resize(1280, 800)
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self.plot = layout.addPlot()
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legend_view = layout.addViewBox()
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legend = pg.LegendItem(offset=(0, 0))
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legend.setParentItem(legend_view)
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color_map = pg.colormap.get('CET-C6s')
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colors = color_map.getLookupTable(nPts=len(TONES))
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t = np.linspace(0, 500, 100)
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self.tone_data = {}
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self.tone_lines = {}
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self.tone_pens = {}
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for tone,color in zip(TONES.keys(), colors):
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self.tone_data[tone] = np.zeros(1000, dtype=np.float64) #np.array([], dtype=np.float64) #np.zeros(int(10000), dtype=np.float64)
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self.tone_lines[tone] = self.plot.plot(self.tone_data[tone], pen=pg.mkPen(color=color), name=tone)
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legend.addItem(self.tone_lines[tone], tone)
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self.tone_pens[tone] = pg.mkPen(color=color)
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self.plot.setLabel('left', 'Signal Correlation')
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self.plot.setLabel('bottom', 'Time (samples)')
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self.plot.showGrid(x=True, y=True)
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legend_view.setFixedWidth(80)
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layout.ci.layout.setColumnFixedWidth(1, 80)
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self.show()
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def set_position(self, pos):
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pass
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def push_tone(self, tone, value):
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self.tone_data[tone] = np.roll(self.tone_data[tone], -1)
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self.tone_data[tone][-1] = value
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self.tone_lines[tone].setData(self.tone_data[tone])
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def push_tones(self, tone, values):
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s = time.perf_counter()
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#self.tone_data[tone] = np.append(self.tone_data[tone], values)
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a = time.perf_counter()
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self.tone_data[tone] = np.roll(self.tone_data[tone], -len(values))
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self.tone_data[tone][-len(values):] = values
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self.tone_lines[tone].setData(self.tone_data[tone])
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#self.tone_lines[tone].setData(values)
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g = time.perf_counter()
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#print(a-s, g-a)
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#self.plot.plot(values, pen=self.tone_pens[tone])
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mkQApp("Correlation matrix display")
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gui = DetectorGui()
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def read_audio_from_stdin(chunk_size, process_chunk):
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global keep_running
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while keep_running:
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# 2 bytes per sample for int16
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read_size = chunk_size * 2
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data = sys.stdin.buffer.read(read_size)
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# Break the loop if no more data is available
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if not data:
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break
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# Convert the binary data to a numpy array of int16
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audio_chunk = np.frombuffer(data, dtype=np.int16)
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process_chunk(audio_chunk)
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sample_rate = 44100
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note_length = 0.5
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guard_duration = 0.01
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def process_audio_chunk(audio_chunk):
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global gui
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data = audio_chunk
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sample_rate = 44100
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s = time.perf_counter()
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data, sample_rate, decimation = anti_alias(data, sample_rate, 4800, 8)
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data = data / (np.max(data) + 0.0001)
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pure_signals = {tone:note(freq, note_length, rate=sample_rate) for tone,freq in TONES.items()}
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aa = time.perf_counter()
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correlations = {tone:smoothing_filter(np.abs(signal.correlate(data, pure, mode='same')), 64) for tone,pure in pure_signals.items()}
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corr = time.perf_counter()
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guard_samples = int(guard_duration * sample_rate)
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for tone,massage in correlations.items():
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#gui.push_tones(tone, massage)
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gui.push_tone(tone, np.mean(massage))
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gui.push_tones(tone, massage[guard_samples:-guard_samples:100])
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draw = time.perf_counter()
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#print(aa-s, corr-aa, draw-corr)
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if draw - s > 0.1:
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print("FAILED")
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if __name__ == '__main__':
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sig.signal(sig.SIGINT, signal_handler)
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chunk_duration = 0.1 # seconds
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sample_rate = 44100
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channels = 1
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chunk_size = int(sample_rate * chunk_duration) * channels
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reader_thread = threading.Thread(target=read_audio_from_stdin, args=(chunk_size, process_audio_chunk))
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reader_thread.daemon = True
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reader_thread.start()
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pg.exec()
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# Wait...
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reader_thread.join()
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@ -5,85 +5,30 @@ import sys
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import numpy as np
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from scipy import signal
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from scipy.io import wavfile
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from scipy.signal import butter, lfilter
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|
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tones = {}
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with open('tones.csv', newline='') as csvfile:
|
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reader = csv.DictReader(csvfile)
|
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for row in reader:
|
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tones[row['designator']] = float(row['frequency'])
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|
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'''
|
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def freq_of_key(midi_key):
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return 440.0 * (2 ** ((midi_key - 69)/12))
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|
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tones = {}
|
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for c in range(65, 90):
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tones[c] = freq_of_key(c)
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'''
|
||||
|
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# Shamelessly lifted from
|
||||
# https://scipy.github.io/old-wiki/pages/Cookbook/ButterworthBandpass
|
||||
def butter_bandpass(lowcut, highcut, fs, order=5):
|
||||
nyq = 0.5 * fs
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low = lowcut / nyq
|
||||
high = highcut / nyq
|
||||
b, a = butter(order, [low, high], btype='band')
|
||||
return b, a
|
||||
|
||||
def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
|
||||
b, a = butter_bandpass(lowcut, highcut, fs, order=order)
|
||||
y = lfilter(b, a, data)
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return y
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||||
|
||||
# tone synthesis
|
||||
def note(freq, cycles, amp=32767.0, rate=44100):
|
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len = cycles * (1.0/rate)
|
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t = np.linspace(0, len, int(len * rate))
|
||||
if freq == 0:
|
||||
data = np.zeros(int(len * rate))
|
||||
else:
|
||||
data = np.sin(2 * np.pi * freq * t) * amp
|
||||
return data.astype(int)
|
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|
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def decimate_from_sample_rate(sample_rate):
|
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if sample_rate == 44100:
|
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return 4 # rate = 11025, Fmax = 5512.5 Hz
|
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elif sample_rate == 48000:
|
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return 5 # rate = 9600, Fmax = 4800 Hz
|
||||
elif sample_rate == 22050:
|
||||
return 2 # rate = 11025, Fmax = 5512.5 Hz
|
||||
elif sample_rate == 11025:
|
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return 1 # rate = 11025, Fmax = 5512.5 Hz
|
||||
else:
|
||||
raise ValueError("Sample rate not supported")
|
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from tones import TONES
|
||||
from filters import bandpass_filter, note, smoothing_filter, anti_alias
|
||||
|
||||
pure_sample_length = 0.1
|
||||
|
||||
if __name__ == '__main__':
|
||||
# TODO JMT: What is this?
|
||||
FLT_LEN = 2000
|
||||
|
||||
file_name = sys.argv[1]
|
||||
sample_rate, data = wavfile.read(file_name)
|
||||
|
||||
print(f"{file_name}: {len(data)} samples @ {sample_rate} Hz")
|
||||
|
||||
decimate = decimate_from_sample_rate(sample_rate)
|
||||
if decimate > 1:
|
||||
data = signal.decimate(data, decimate)
|
||||
sample_rate = sample_rate / decimate
|
||||
if len(data.shape) == 2:
|
||||
data = data.mean(axis=1)
|
||||
|
||||
print(f'Length after decimation: {len(data)} samples')
|
||||
# Coarse normalisation
|
||||
data = data / np.max(data)
|
||||
|
||||
data = butter_bandpass_filter(data, 270, 1700, sample_rate, order=8)
|
||||
# TODO JMT: Find out why the correlation step fails when max frequency <= 2 * nyquist rate
|
||||
data, sample_rate, decimation = anti_alias(data, sample_rate, 4800)
|
||||
print(f'Length after decimation: {len(data)} samples (/{decimation}, {sample_rate} Hz)')
|
||||
|
||||
pure_signals = {tone:note(freq, FLT_LEN, rate=sample_rate) for tone,freq in tones.items()}
|
||||
pure_signals = {tone:note(freq, pure_sample_length, rate=sample_rate) for tone,freq in TONES.items()}
|
||||
correlations = {tone:np.abs(signal.correlate(data, pure, mode='same')) for tone,pure in pure_signals.items()}
|
||||
massaged = {tone:smoothing_filter(correlation) for tone,correlation in correlations.items()}
|
||||
|
||||
N = FLT_LEN // 8 # Rolling average length
|
||||
cumsum_convolution = np.ones(N)/N
|
||||
massaged = {tone:np.convolve(correlation, cumsum_convolution, mode='valid') for tone,correlation in correlations.items()}
|
||||
|
||||
# Only import if we're actually plotting, these imports are pretty heavy.
|
||||
import pyqtgraph as pg
|
||||
@ -100,7 +45,7 @@ if __name__ == '__main__':
|
||||
legend.setParentItem(legend_view)
|
||||
|
||||
color_map = pg.colormap.get('CET-C6s')
|
||||
colors = color_map.getLookupTable(nPts=len(tones))
|
||||
colors = color_map.getLookupTable(nPts=len(TONES))
|
||||
|
||||
for (tone, correlation), color in zip(massaged.items(), colors):
|
||||
line = plot.plot(correlation, pen=pg.mkPen(color=color), fillLevel=0.1, name=tone)
|
||||
|
||||
@ -1,339 +0,0 @@
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
Version 2, June 1991
|
||||
|
||||
Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
|
||||
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The licenses for most software are designed to take away your
|
||||
freedom to share and change it. By contrast, the GNU General Public
|
||||
License is intended to guarantee your freedom to share and change free
|
||||
software--to make sure the software is free for all its users. This
|
||||
General Public License applies to most of the Free Software
|
||||
Foundation's software and to any other program whose authors commit to
|
||||
using it. (Some other Free Software Foundation software is covered by
|
||||
the GNU Lesser General Public License instead.) You can apply it to
|
||||
your programs, too.
|
||||
|
||||
When we speak of free software, we are referring to freedom, not
|
||||
price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
this service if you wish), that you receive source code or can get it
|
||||
if you want it, that you can change the software or use pieces of it
|
||||
in new free programs; and that you know you can do these things.
|
||||
|
||||
To protect your rights, we need to make restrictions that forbid
|
||||
anyone to deny you these rights or to ask you to surrender the rights.
|
||||
These restrictions translate to certain responsibilities for you if you
|
||||
distribute copies of the software, or if you modify it.
|
||||
|
||||
For example, if you distribute copies of such a program, whether
|
||||
gratis or for a fee, you must give the recipients all the rights that
|
||||
you have. You must make sure that they, too, receive or can get the
|
||||
source code. And you must show them these terms so they know their
|
||||
rights.
|
||||
|
||||
We protect your rights with two steps: (1) copyright the software, and
|
||||
(2) offer you this license which gives you legal permission to copy,
|
||||
distribute and/or modify the software.
|
||||
|
||||
Also, for each author's protection and ours, we want to make certain
|
||||
that everyone understands that there is no warranty for this free
|
||||
software. If the software is modified by someone else and passed on, we
|
||||
want its recipients to know that what they have is not the original, so
|
||||
that any problems introduced by others will not reflect on the original
|
||||
authors' reputations.
|
||||
|
||||
Finally, any free program is threatened constantly by software
|
||||
patents. We wish to avoid the danger that redistributors of a free
|
||||
program will individually obtain patent licenses, in effect making the
|
||||
program proprietary. To prevent this, we have made it clear that any
|
||||
patent must be licensed for everyone's free use or not licensed at all.
|
||||
|
||||
The precise terms and conditions for copying, distribution and
|
||||
modification follow.
|
||||
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
|
||||
|
||||
0. This License applies to any program or other work which contains
|
||||
a notice placed by the copyright holder saying it may be distributed
|
||||
under the terms of this General Public License. The "Program", below,
|
||||
refers to any such program or work, and a "work based on the Program"
|
||||
means either the Program or any derivative work under copyright law:
|
||||
that is to say, a work containing the Program or a portion of it,
|
||||
either verbatim or with modifications and/or translated into another
|
||||
language. (Hereinafter, translation is included without limitation in
|
||||
the term "modification".) Each licensee is addressed as "you".
|
||||
|
||||
Activities other than copying, distribution and modification are not
|
||||
covered by this License; they are outside its scope. The act of
|
||||
running the Program is not restricted, and the output from the Program
|
||||
is covered only if its contents constitute a work based on the
|
||||
Program (independent of having been made by running the Program).
|
||||
Whether that is true depends on what the Program does.
|
||||
|
||||
1. You may copy and distribute verbatim copies of the Program's
|
||||
source code as you receive it, in any medium, provided that you
|
||||
conspicuously and appropriately publish on each copy an appropriate
|
||||
copyright notice and disclaimer of warranty; keep intact all the
|
||||
notices that refer to this License and to the absence of any warranty;
|
||||
and give any other recipients of the Program a copy of this License
|
||||
along with the Program.
|
||||
|
||||
You may charge a fee for the physical act of transferring a copy, and
|
||||
you may at your option offer warranty protection in exchange for a fee.
|
||||
|
||||
2. You may modify your copy or copies of the Program or any portion
|
||||
of it, thus forming a work based on the Program, and copy and
|
||||
distribute such modifications or work under the terms of Section 1
|
||||
above, provided that you also meet all of these conditions:
|
||||
|
||||
a) You must cause the modified files to carry prominent notices
|
||||
stating that you changed the files and the date of any change.
|
||||
|
||||
b) You must cause any work that you distribute or publish, that in
|
||||
whole or in part contains or is derived from the Program or any
|
||||
part thereof, to be licensed as a whole at no charge to all third
|
||||
parties under the terms of this License.
|
||||
|
||||
c) If the modified program normally reads commands interactively
|
||||
when run, you must cause it, when started running for such
|
||||
interactive use in the most ordinary way, to print or display an
|
||||
announcement including an appropriate copyright notice and a
|
||||
notice that there is no warranty (or else, saying that you provide
|
||||
a warranty) and that users may redistribute the program under
|
||||
these conditions, and telling the user how to view a copy of this
|
||||
License. (Exception: if the Program itself is interactive but
|
||||
does not normally print such an announcement, your work based on
|
||||
the Program is not required to print an announcement.)
|
||||
|
||||
These requirements apply to the modified work as a whole. If
|
||||
identifiable sections of that work are not derived from the Program,
|
||||
and can be reasonably considered independent and separate works in
|
||||
themselves, then this License, and its terms, do not apply to those
|
||||
sections when you distribute them as separate works. But when you
|
||||
distribute the same sections as part of a whole which is a work based
|
||||
on the Program, the distribution of the whole must be on the terms of
|
||||
this License, whose permissions for other licensees extend to the
|
||||
entire whole, and thus to each and every part regardless of who wrote it.
|
||||
|
||||
Thus, it is not the intent of this section to claim rights or contest
|
||||
your rights to work written entirely by you; rather, the intent is to
|
||||
exercise the right to control the distribution of derivative or
|
||||
collective works based on the Program.
|
||||
|
||||
In addition, mere aggregation of another work not based on the Program
|
||||
with the Program (or with a work based on the Program) on a volume of
|
||||
a storage or distribution medium does not bring the other work under
|
||||
the scope of this License.
|
||||
|
||||
3. You may copy and distribute the Program (or a work based on it,
|
||||
under Section 2) in object code or executable form under the terms of
|
||||
Sections 1 and 2 above provided that you also do one of the following:
|
||||
|
||||
a) Accompany it with the complete corresponding machine-readable
|
||||
source code, which must be distributed under the terms of Sections
|
||||
1 and 2 above on a medium customarily used for software interchange; or,
|
||||
|
||||
b) Accompany it with a written offer, valid for at least three
|
||||
years, to give any third party, for a charge no more than your
|
||||
cost of physically performing source distribution, a complete
|
||||
machine-readable copy of the corresponding source code, to be
|
||||
distributed under the terms of Sections 1 and 2 above on a medium
|
||||
customarily used for software interchange; or,
|
||||
|
||||
c) Accompany it with the information you received as to the offer
|
||||
to distribute corresponding source code. (This alternative is
|
||||
allowed only for noncommercial distribution and only if you
|
||||
received the program in object code or executable form with such
|
||||
an offer, in accord with Subsection b above.)
|
||||
|
||||
The source code for a work means the preferred form of the work for
|
||||
making modifications to it. For an executable work, complete source
|
||||
code means all the source code for all modules it contains, plus any
|
||||
associated interface definition files, plus the scripts used to
|
||||
control compilation and installation of the executable. However, as a
|
||||
special exception, the source code distributed need not include
|
||||
anything that is normally distributed (in either source or binary
|
||||
form) with the major components (compiler, kernel, and so on) of the
|
||||
operating system on which the executable runs, unless that component
|
||||
itself accompanies the executable.
|
||||
|
||||
If distribution of executable or object code is made by offering
|
||||
access to copy from a designated place, then offering equivalent
|
||||
access to copy the source code from the same place counts as
|
||||
distribution of the source code, even though third parties are not
|
||||
compelled to copy the source along with the object code.
|
||||
|
||||
4. You may not copy, modify, sublicense, or distribute the Program
|
||||
except as expressly provided under this License. Any attempt
|
||||
otherwise to copy, modify, sublicense or distribute the Program is
|
||||
void, and will automatically terminate your rights under this License.
|
||||
However, parties who have received copies, or rights, from you under
|
||||
this License will not have their licenses terminated so long as such
|
||||
parties remain in full compliance.
|
||||
|
||||
5. You are not required to accept this License, since you have not
|
||||
signed it. However, nothing else grants you permission to modify or
|
||||
distribute the Program or its derivative works. These actions are
|
||||
prohibited by law if you do not accept this License. Therefore, by
|
||||
modifying or distributing the Program (or any work based on the
|
||||
Program), you indicate your acceptance of this License to do so, and
|
||||
all its terms and conditions for copying, distributing or modifying
|
||||
the Program or works based on it.
|
||||
|
||||
6. Each time you redistribute the Program (or any work based on the
|
||||
Program), the recipient automatically receives a license from the
|
||||
original licensor to copy, distribute or modify the Program subject to
|
||||
these terms and conditions. You may not impose any further
|
||||
restrictions on the recipients' exercise of the rights granted herein.
|
||||
You are not responsible for enforcing compliance by third parties to
|
||||
this License.
|
||||
|
||||
7. If, as a consequence of a court judgment or allegation of patent
|
||||
infringement or for any other reason (not limited to patent issues),
|
||||
conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot
|
||||
distribute so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you
|
||||
may not distribute the Program at all. For example, if a patent
|
||||
license would not permit royalty-free redistribution of the Program by
|
||||
all those who receive copies directly or indirectly through you, then
|
||||
the only way you could satisfy both it and this License would be to
|
||||
refrain entirely from distribution of the Program.
|
||||
|
||||
If any portion of this section is held invalid or unenforceable under
|
||||
any particular circumstance, the balance of the section is intended to
|
||||
apply and the section as a whole is intended to apply in other
|
||||
circumstances.
|
||||
|
||||
It is not the purpose of this section to induce you to infringe any
|
||||
patents or other property right claims or to contest validity of any
|
||||
such claims; this section has the sole purpose of protecting the
|
||||
integrity of the free software distribution system, which is
|
||||
implemented by public license practices. Many people have made
|
||||
generous contributions to the wide range of software distributed
|
||||
through that system in reliance on consistent application of that
|
||||
system; it is up to the author/donor to decide if he or she is willing
|
||||
to distribute software through any other system and a licensee cannot
|
||||
impose that choice.
|
||||
|
||||
This section is intended to make thoroughly clear what is believed to
|
||||
be a consequence of the rest of this License.
|
||||
|
||||
8. If the distribution and/or use of the Program is restricted in
|
||||
certain countries either by patents or by copyrighted interfaces, the
|
||||
original copyright holder who places the Program under this License
|
||||
may add an explicit geographical distribution limitation excluding
|
||||
those countries, so that distribution is permitted only in or among
|
||||
countries not thus excluded. In such case, this License incorporates
|
||||
the limitation as if written in the body of this License.
|
||||
|
||||
9. The Free Software Foundation may publish revised and/or new versions
|
||||
of the General Public License from time to time. Such new versions will
|
||||
be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the Program
|
||||
specifies a version number of this License which applies to it and "any
|
||||
later version", you have the option of following the terms and conditions
|
||||
either of that version or of any later version published by the Free
|
||||
Software Foundation. If the Program does not specify a version number of
|
||||
this License, you may choose any version ever published by the Free Software
|
||||
Foundation.
|
||||
|
||||
10. If you wish to incorporate parts of the Program into other free
|
||||
programs whose distribution conditions are different, write to the author
|
||||
to ask for permission. For software which is copyrighted by the Free
|
||||
Software Foundation, write to the Free Software Foundation; we sometimes
|
||||
make exceptions for this. Our decision will be guided by the two goals
|
||||
of preserving the free status of all derivatives of our free software and
|
||||
of promoting the sharing and reuse of software generally.
|
||||
|
||||
NO WARRANTY
|
||||
|
||||
11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
|
||||
FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
|
||||
OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
|
||||
PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
|
||||
OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
|
||||
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
|
||||
TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
|
||||
PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
|
||||
REPAIR OR CORRECTION.
|
||||
|
||||
12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
|
||||
REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
|
||||
INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
|
||||
OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
|
||||
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
|
||||
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
|
||||
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
|
||||
POSSIBILITY OF SUCH DAMAGES.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
to attach them to the start of each source file to most effectively
|
||||
convey the exclusion of warranty; and each file should have at least
|
||||
the "copyright" line and a pointer to where the full notice is found.
|
||||
|
||||
{description}
|
||||
Copyright (C) {year} {fullname}
|
||||
|
||||
This program is free software; you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
|
||||
the Free Software Foundation; either version 2 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU General Public License along
|
||||
with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If the program is interactive, make it output a short notice like this
|
||||
when it starts in an interactive mode:
|
||||
|
||||
Gnomovision version 69, Copyright (C) year name of author
|
||||
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
||||
This is free software, and you are welcome to redistribute it
|
||||
under certain conditions; type `show c' for details.
|
||||
|
||||
The hypothetical commands `show w' and `show c' should show the appropriate
|
||||
parts of the General Public License. Of course, the commands you use may
|
||||
be called something other than `show w' and `show c'; they could even be
|
||||
mouse-clicks or menu items--whatever suits your program.
|
||||
|
||||
You should also get your employer (if you work as a programmer) or your
|
||||
school, if any, to sign a "copyright disclaimer" for the program, if
|
||||
necessary. Here is a sample; alter the names:
|
||||
|
||||
Yoyodyne, Inc., hereby disclaims all copyright interest in the program
|
||||
`Gnomovision' (which makes passes at compilers) written by James Hacker.
|
||||
|
||||
{signature of Ty Coon}, 1 April 1989
|
||||
Ty Coon, President of Vice
|
||||
|
||||
This General Public License does not permit incorporating your program into
|
||||
proprietary programs. If your program is a subroutine library, you may
|
||||
consider it more useful to permit linking proprietary applications with the
|
||||
library. If this is what you want to do, use the GNU Lesser General
|
||||
Public License instead of this License.
|
||||
@ -1,308 +0,0 @@
|
||||
selcald
|
||||
=======
|
||||
|
||||
Selcal decoder daemon
|
||||
---------------------
|
||||
|
||||
|
||||
|
||||
A Linux/BSD daemon that monitors an audio stream and looks for selcal
|
||||
(Selective Calling; see <https://en.wikipedia.org/wiki/SELCAL>) calls and
|
||||
emits a timestamp, followed by the selcal code received. The daemon is
|
||||
intended to be as simple and lightweight as possible, and should rely
|
||||
on existing frameworks such as fftw where possible.
|
||||
|
||||
[Selective Calling (SELCAL)][1]
|
||||
--------------------------
|
||||
|
||||
SELCAL is a technique that allows a ground radio operator to alert an
|
||||
aircrew that the operator wishes to communicate with that aircraft.
|
||||
|
||||
Because of the background noise level experienced on HF radio frequencies,
|
||||
aircrews usually prefer to turn down the audio level of their HF receiver
|
||||
until alerted via SELCAL of a message specifically intended for their
|
||||
aircraft. When the ground station operator wishes to communicate with an
|
||||
aircraft, he enters into the SELCAL encoder the 4-letter code of that aircraft,
|
||||
which is usually included in its flight plan, and transmits that code over the
|
||||
assigned radio channel. All aircraft monitoring that channel receive the
|
||||
SELCAL broadcast, but only those (preferably only one) that have been
|
||||
programmed with that 4-letter code will respond by sounding a chime or
|
||||
otherwise alerting the crew. The crew will then set their volume control
|
||||
higher to listen to the voice traffic and, using ICAO recommended radio
|
||||
procedures, assure that the message is intended for them.
|
||||
|
||||
[Selcal Specification][2]
|
||||
--------------------
|
||||
The official specification for the selcal system is found in
|
||||
"ARINC Characteristic 714-6-1990", published on August 15, 1990. The key
|
||||
attributes of selcal codes are as follows:
|
||||
|
||||
### General
|
||||
|
||||
Selective calling is accomplished by the coder of the ground transmitter
|
||||
sending coded tone pulses to the aircraft receiver and decoder. Each
|
||||
transmitted code is made up of two consecutive tone pulses, with each pulse
|
||||
containing two simultaneously-transmitted tones.
|
||||
|
||||
### Transmitted Code
|
||||
|
||||
When the ground operator desires to call a particular aircraft, he depresses
|
||||
the buttons corresponding to the code assigned to that aircraft. The coder
|
||||
then keys the transmitter on the air causing to be transmitted two
|
||||
consecutive tone pulses of 1.0 +/- 0.25 sec. duration, separated by an
|
||||
interval of 0.2 +/- 0.1 sec. which makes up the code. Each tone pulse
|
||||
consists of two simultaneously-transmitted tones. The call should consist
|
||||
of one transmitted code without repetition.
|
||||
|
||||
### Stability
|
||||
|
||||
The frequency of transmitted codes should be held to +/- 0.15% tolerance to
|
||||
insure proper operation of the airborne decoder.
|
||||
|
||||
**NOTE:** The specification does not indicate the required frequency accuracy of
|
||||
the receiver. Given that [research][3] seems to [show][4] that doppler spreads of
|
||||
5-20 Hz over polar paths are possible, it seems that as a practical matter,
|
||||
the receiver frequency tolerances have to be more relaxed than the transmitter
|
||||
frequency tolerances. In addition, practical experience has shown that various
|
||||
ground stations do not appear to follow the +/- 0.15% tolerance regardless. An
|
||||
initial estimate of a receiver tolerance of 2-2.5% for the tones should be
|
||||
sufficient to mitigate transmitter, receiver, and sound card frequency errors.
|
||||
This issue more or less disappears if the source of audio is an AM receiver,
|
||||
rather than a SSB one, since the SELCALs are sent as SC-USB, they can be
|
||||
demodulated with an AM receiver with no frequency error, other than the transmitter
|
||||
and ionospheric contributions. Based on a cursory analysis of the live
|
||||
data available, it seems that it is quite easy to have a combination of
|
||||
receiver frequency and soundcard clock errors that sum to 50 Hz or more.
|
||||
This easily puts the tones out of any reasonable detection band.
|
||||
|
||||
### Distortion
|
||||
|
||||
Overall audio distortion present on the transmitted RF signal should not
|
||||
exceed 15%.
|
||||
|
||||
### Percent Modulation
|
||||
|
||||
The RF signals transmitted by the ground radio station should contain within
|
||||
3 dB of equal amounts of the two modulating tones. The combination of tones
|
||||
should result in a modulation envelope having a nominal modulation percentage
|
||||
of 90% and in no case less than 60%.
|
||||
|
||||
### Transmitted Tones
|
||||
|
||||
Tone codes are made up of various combinations of the following tones and
|
||||
are designated by letter as indicated:
|
||||
|
||||
Note: The tones are spaced by 10^(0.045)-1 (approximately 10.9%)
|
||||
|
||||
| Designation | Nominal Frequency (Hz) | Minimum | Maximum | Width |
|
||||
| ----------- | ---------------------- | -------- | -------- | ----- |
|
||||
| A | 312.60 | 312.13 | 313.07 | 0.94 |
|
||||
| B | 346.70 | 346.18 | 347.22 | 1.04 |
|
||||
| C | 384.60 | 384.02 | 385.18 | 1.15 |
|
||||
| D | 426.60 | 425.96 | 427.24 | 1.28 |
|
||||
| E | 473.20 | 472.49 | 473.91 | 1.42 |
|
||||
| F | 524.80 | 524.01 | 525.59 | 1.57 |
|
||||
| G | 582.10 | 581.23 | 582.97 | 1.75 |
|
||||
| H | 645.70 | 644.73 | 646.67 | 1.94 |
|
||||
| J | 716.10 | 715.03 | 717.17 | 2.15 |
|
||||
| K | 794.30 | 793.11 | 795.49 | 2.38 |
|
||||
| L | 881.00 | 879.68 | 882.32 | 2.64 |
|
||||
| M | 977.20 | 975.73 | 978.67 | 2.93 |
|
||||
| P | 1,083.90 | 1,082.27 | 1,085.53 | 3.25 |
|
||||
| Q | 1,202.30 | 1,200.50 | 1,204.10 | 3.61 |
|
||||
| R | 1,333.50 | 1,331.50 | 1,335.50 | 4.00 |
|
||||
| S | 1,479.10 | 1,476.88 | 1,481.32 | 4.44 |
|
||||
|
||||
|
||||
### Received Tones
|
||||
|
||||
| Designation | Nominal Frequency | Low | High | Width | Guard |
|
||||
| ----------- | ----------------- | --- | ---- | ----- | ----- |
|
||||
| A | 312.60 | 306.35 | 318.85 | 12.50 | 20.91 |
|
||||
| B | 346.70 | 339.77 | 353.63 | 13.87 | 23.27 |
|
||||
| C | 384.60 | 376.91 | 392.29 | 15.38 | 25.78 |
|
||||
| D | 426.60 | 418.07 | 435.13 | 17.06 | 28.60 |
|
||||
| E | 473.20 | 463.74 | 482.66 | 18.93 | 31.64 |
|
||||
| F | 524.80 | 514.30 | 535.30 | 20.99 | 35.16 |
|
||||
| G | 582.10 | 570.46 | 593.74 | 23.28 | 39.04 |
|
||||
| H | 645.70 | 632.79 | 658.61 | 25.83 | 43.16 |
|
||||
| J | 716.10 | 701.78 | 730.42 | 28.64 | 47.99 |
|
||||
| K | 794.30 | 778.41 | 810.19 | 31.77 | 53.19 |
|
||||
| L | 881.00 | 863.38 | 898.62 | 35.24 | 59.04 |
|
||||
| M | 977.20 | 957.66 | 996.74 | 39.09 | 65.48 |
|
||||
| P | 1,083.90 | 1,062.22 | 1,105.58 | 43.36 | 72.68 |
|
||||
| Q | 1,202.30 | 1,178.25 | 1,226.35 | 48.09 | 80.48 |
|
||||
| R | 1,333.50 | 1,306.83 | 1,360.17 | 53.34 | 89.35 |
|
||||
| S | 1,479.10 | 1,449.52 | 1,508.68 | 59.16 | -- |
|
||||
|
||||
### Table of Tone Frequencies and Derivation of the Frequencies
|
||||
|
||||
fN = 10^((N-1) x 0.045 + 2). For the first tone, N=12, second N=13, etc.
|
||||
|
||||
| Designation | Log Frequency | Frequency (Hz) |
|
||||
| ----------- | ------------- | -------------- |
|
||||
| A | 2.495 | 312.6 |
|
||||
| B | 2.540 | 346.7 |
|
||||
| C | 2.585 | 384.6 |
|
||||
| D | 2.630 | 426.6 |
|
||||
| E | 2.675 | 473.2 |
|
||||
| F | 2.720 | 524.8 |
|
||||
| G | 2.765 | 582.1 |
|
||||
| H | 2.810 | 645.7 |
|
||||
| J | 2.855 | 716.1 |
|
||||
| K | 2.900 | 794.3 |
|
||||
| L | 2.945 | 881.0 |
|
||||
| M | 2.990 | 977.2 |
|
||||
| P | 3.035 | 1083.9 |
|
||||
| Q | 3.080 | 1202.3 |
|
||||
| R | 3.125 | 1333.5 |
|
||||
| S | 3.170 | 1479.1 |
|
||||
|
||||
Challenges for SSB Receivers
|
||||
----------------------------
|
||||
|
||||
It is important to note that "real" selcal receivers use AM demodulators to
|
||||
receive the suppressed carrier (SC) transmissions from the ground stations, and
|
||||
so for them, the audio tones received will be effectively the same as those
|
||||
transmitted by the ground stations. The tones are then decoded and used to
|
||||
either signal the user of the call, or to automatically unmute a separate SSB
|
||||
receiver.
|
||||
|
||||
For hobbyist uses, there is typically only one receiver, and it is a SSB
|
||||
receiver. This adds additional errors to the received tones, based on offsets
|
||||
in the tuned frequency and the carrier injection (BFO) frequency. One possible
|
||||
solution to this problem is to step back from trying to determine the
|
||||
frequencies of the individual tones and to instead verify that:
|
||||
|
||||
1. There are two tones present
|
||||
2. The difference in frequency between the two tones matches a pair of known
|
||||
tones in the alphabet
|
||||
|
||||
### Table of Tone Frequency Differences
|
||||
|
||||
| Tone | Frequency | A | B | C | D | E | F | G | H | J | K | L | M | P | Q | R | S |
|
||||
| --------- | --------- | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
|
||||
| | | 312.6 | 346.7 | 384.6 | 426.6 | 473.2 | 524.8 | 582.1 | 645.7 | 716.1 | 794.3 | 881.0 | 977.2 | 1083.9 | 1202.3 | 1333.5 | 1479.1 |
|
||||
| A | 312.6 | 0.0 | | | | | | | | | | | | | | | |
|
||||
| B | 346.7 | 34.1 | 0.0 | | | | | | | | | | | | | | |
|
||||
| C | 384.6 | 72.0 | 37.9 | 0.0 | | | | | | | | | | | | | |
|
||||
| D | 426.6 | 114.0 | 79.9 | 42.0 | 0.0 | | | | | | | | | | | | |
|
||||
| E | 473.2 | 160.6 | 126.5 | 88.6 | 46.6 | 0.0 | | | | | | | | | | | |
|
||||
| F | 524.8 | 212.2 | 178.1 | 140.2 | 98.2 | 51.6 | 0.0 | | | | | | | | | | |
|
||||
| G | 582.1 | 269.5 | 235.4 | 197.5 | 155.5 | 108.9 | 57.3 | 0.0 | | | | | | | | | |
|
||||
| H | 645.7 | 333.1 | 299.0 | 261.1 | 219.1 | 172.5 | 120.9 | 63.6 | 0.0 | | | | | | | | |
|
||||
| J | 716.1 | 403.5 | 369.4 | 331.5 | 289.5 | 242.9 | 191.3 | 134.0 | 70.4 | 0.0 | | | | | | | |
|
||||
| K | 794.3 | 481.7 | 447.6 | 409.7 | 367.7 | 321.1 | 269.5 | 212.2 | 148.6 | 78.2 | 0.0 | | | | | | |
|
||||
| L | 881.0 | 568.4 | 534.3 | 496.4 | 454.4 | 407.8 | 356.2 | 298.9 | 235.3 | 164.9 | 86.7 | 0.0 | | | | | |
|
||||
| M | 977.2 | 664.6 | 630.5 | 592.6 | 550.6 | 504.0 | 452.4 | 395.1 | 331.5 | 261.1 | 182.9 | 96.2 | 0.0 | | | | |
|
||||
| P | 1083.9 | 771.3 | 737.2 | 699.3 | 657.3 | 610.7 | 559.1 | 501.8 | 438.2 | 367.8 | 289.6 | 202.9 | 106.7 | 0.0 | | | |
|
||||
| Q | 1202.3 | 889.7 | 855.6 | 817.7 | 775.7 | 729.1 | 677.5 | 620.2 | 556.6 | 486.2 | 408.0 | 321.3 | 225.1 | 118.4 | 0.0 | | |
|
||||
| R | 1333.5 | 1020.9 | 986.8 | 948.9 | 906.9 | 860.3 | 808.7 | 751.4 | 687.8 | 617.4 | 539.2 | 452.5 | 356.3 | 249.6 | 131.2 | 0.0 | |
|
||||
| S | 1479.1 | 1166.5 | 1132.4 | 1094.5 | 1052.5 | 1005.9 | 954.3 | 897.0 | 833.4 | 763.0 | 684.8 | 598.1 | 501.9 | 395.2 | 276.8 | 145.6 | 0.0 |
|
||||
|
||||
Signal Processing
|
||||
-----------------
|
||||
|
||||
Detection of the selcal tones is quite similar to DTMF tone detection, and
|
||||
this has been well documented. There are several approaches available:
|
||||
|
||||
1. Bandpass filter bank and energy detectors (i.e. analog implementation approach)
|
||||
2. Discrete FFT and energy detection in bins containing selcal tones
|
||||
3. Goertzl algorithm for fast DFT (see <https://en.wikipedia.org/wiki/Goertzel_algorithm>)
|
||||
4. Chirp-Z transform for DFT (see <https://en.wikipedia.org/wiki/Bluestein%27s_FFT_algorithm>)
|
||||
5. MUSIC algorithm (see <https://en.wikipedia.org/wiki/Multiple_signal_classification>)
|
||||
6. ESPRIT algorithm (see <https://en.wikipedia.org/wiki/Estimation_of_signal_parameters_via_rotational_invariance_techniques>)
|
||||
7. Wavelet transform and convolution (seems highly advanced)
|
||||
8. Q Transform (handles geometric spacing of tones/bins much better)
|
||||
|
||||
The implementation should take into account characteristics of the HF radio medium:
|
||||
|
||||
* Often poor signal/noise ratio
|
||||
* Frequent ionospheric and auroral fading and flutter
|
||||
* Slight doppler due to relative ionospheric motion
|
||||
|
||||
These imply that unlike DTMF decoder implementations, a series of measurements should
|
||||
be made during the signal and a final decision determined from statistical analysis
|
||||
of the raw measurements. Based on the highest baseband frequency of approximately
|
||||
1500 Hz, this means that any audio sampling rate above 4000 samples/second should work
|
||||
fine. Looking at the post-detection stage, we can see that the gap between the two tone
|
||||
groups is specified as 200 mS +/- 100 mS, so in the worst case, the gap could be 100 mS.
|
||||
Following Nyquist and sampling theory, this means that in that 100 mS period, we need to
|
||||
check for the presence or absence of signal at least twice, or once every 50 mS. The actual
|
||||
numbers will vary based on audio sampling rates, but for the typical 44100 samples/second
|
||||
rate of modern sound cards, this leads us to use a block size of 2048 (using a nice round
|
||||
binary number) and a post-processing sample rate of 2048/44100 or 46.4 mS. This also means
|
||||
that we need to window this sample size appropriately and choose an algorithm for tone
|
||||
detection that is "comfortable" with this block size.
|
||||
|
||||
After considerable trolling through the Internet, and discarding many approaches
|
||||
(see above) that are oriented toward detecting _unknown_ tones, it seems that
|
||||
theoretically, the ideal approach is to use [cross-correlation][5] of the input signal with
|
||||
_known_ signals to detect their presence, otherwise known as [matched filtering][6].
|
||||
|
||||
The figure below shows the cross-correlation between the received SELCAL "JRAE" and each
|
||||
of the 16 tones in the alphabet. The lines are ordered from tone A on the top to tone
|
||||
S on the bottom, and 2.5 to 3.0 seconds of time run from left to right.
|
||||
|
||||
|
||||
|
||||
Some care must be taken in selecting the block size, as it is a tradeoff between
|
||||
integrating more signal power to improve cross-correlation detection, and added
|
||||
difficulty in detecting the silent period between the tone groups. As mentioned
|
||||
earlier, presumably for direct detection of the shortest possible silent period,
|
||||
blocks should be less than 50 mS, but this would result in requiring detection
|
||||
based on only 15 cycles (312.6 Hz * 0.05 sec) for the lowest frequency tone.
|
||||
|
||||
|
||||
|
||||
The normal source of sampled audio is the soundcard interface.
|
||||
After some digging, it seems that PortAudio would be a good choice for an audio interface
|
||||
API, since it provides both a degree of platform independence and isolates the application
|
||||
from the various underlying audio frameworks (i.e. ALSA, Pulseaudio, SndKit, etc.).
|
||||
In general, lower sampling rates are preferred due to the reduced processing load,
|
||||
as are fixed point DSP implementations versus floating point implementations.
|
||||
|
||||
### Pseudocode
|
||||
|
||||
Clear the result buffer
|
||||
Clear the tones
|
||||
Set state to idle
|
||||
Set the threshold to 50%
|
||||
While true
|
||||
Capture one block of audio (~100 mS)
|
||||
Decimate it by 10
|
||||
For each tone in the alphabet
|
||||
Cross correlate the audio with the tone
|
||||
If the correlation is greater than the threshold
|
||||
Save the result in the buffer
|
||||
Else
|
||||
Add the correlation to the threshold and average
|
||||
If there are > 800 mS of results in the buffer
|
||||
If the last result was silence (no tones)
|
||||
For each tone in the alphabet
|
||||
If there is a majority of results in the buffer
|
||||
Then the current tone is detected
|
||||
If two tones are detected
|
||||
If the state is idle
|
||||
Set the state to two tones
|
||||
Save the two tones
|
||||
If the state is two tones
|
||||
Return the four tones
|
||||
Clear the result buffer
|
||||
Clear the tones
|
||||
Set the state to idle
|
||||
|
||||
References
|
||||
----------
|
||||
|
||||
[^1]: http://www.asri.aero/our-services/selcal/ "Aviation Spectrum Resources Inc. website, retrieved 3, Nov 2013"
|
||||
|
||||
[^2]: http://store.aviation-ia.com/cf/store/catalog_detail.cfm?item_id=126 "ARINC Characteristic 714-6-1990, chapter 2; August 15, 1990"
|
||||
|
||||
[^3]: https://lra.le.ac.uk/bitstream/2381/7403/3/propagation%20of%20HF%20radio%20waves.pdf "Propagation of HF radio waves over northerly paths: measurements, simulation and systems aspects, Warrington et al"
|
||||
|
||||
[^4]: http://onlinelibrary.wiley.com/doi/10.1002/2013RS005264/full "Observations of Doppler and delay spreads on HF signals received over polar cap and trough paths at various stages of the solar cycle, Stocker, A. J., E. M. Warrington, and D. R. Siddle (2013), Radio Sci., 48, 638–645, doi:10.1002/2013RS005264"
|
||||
|
||||
[^5]: https://en.wikipedia.org/wiki/Autocorrelation "Autocorrelation"
|
||||
|
||||
[^6]: https://en.wikipedia.org/wiki/Matched_filter "Matched filter"
|
||||
@ -1,49 +0,0 @@
|
||||
from __future__ import print_function
|
||||
|
||||
class Alphabet:
|
||||
|
||||
TONES = dict({'Alpha': 312.6,
|
||||
'Bravo': 346.7,
|
||||
'Charlie': 384.6,
|
||||
'Delta': 426.6,
|
||||
'Echo': 473.2,
|
||||
'Foxtrot': 524.8,
|
||||
'Golf': 582.1,
|
||||
'Hotel': 645.7,
|
||||
'Juliette': 716.1,
|
||||
'Kilo': 794.3,
|
||||
'Lima': 881.0,
|
||||
'Mike': 977.2,
|
||||
'Papa': 1083.9,
|
||||
'Quebec': 1202.3,
|
||||
'Romeo': 1333.5,
|
||||
'Sierra': 1479.1, })
|
||||
|
||||
def frequency(self, name='Alpha'):
|
||||
try:
|
||||
return Alphabet.TONES[name]
|
||||
except KeyError:
|
||||
for tone in Alphabet.TONES:
|
||||
if str(tone[0]).upper() == str(name).upper():
|
||||
return Alphabet.TONES[tone]
|
||||
return None
|
||||
|
||||
def tone(self, frequency=TONES['Alpha'], accuracy=0.02):
|
||||
for tone in Alphabet.TONES:
|
||||
if abs((frequency / Alphabet.TONES[tone]) - 1) <= accuracy:
|
||||
return tone
|
||||
return None
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
x = Alphabet()
|
||||
print(x.frequency())
|
||||
print(x.frequency('Sierra'))
|
||||
print(x.frequency('Foobar'))
|
||||
print(x.frequency('H'))
|
||||
|
||||
print(x.tone(312.6))
|
||||
print(x.tone(1479.1))
|
||||
print(x.tone(500.0))
|
||||
print(x.tone(500.0, 0.05))
|
||||
print(x.tone(714.7))
|
||||
@ -1,251 +0,0 @@
|
||||
# Run with "ipython -i --matplotlib=qt analyze.py <file>.wav"
|
||||
#
|
||||
from __future__ import print_function
|
||||
import sys
|
||||
import numpy as np
|
||||
# import pandas as pd
|
||||
from scipy import signal
|
||||
import matplotlib.pyplot as plt
|
||||
from scipy.io.wavfile import read
|
||||
from scipy.signal import butter, lfilter
|
||||
from math import log10
|
||||
|
||||
FRAME = 0.1 # Frame time in seconds
|
||||
|
||||
Alpha = 312.6
|
||||
Bravo = 346.7
|
||||
Charlie = 384.6
|
||||
Delta = 426.6
|
||||
Echo = 473.2
|
||||
Foxtrot = 524.8
|
||||
Golf = 582.1
|
||||
Hotel = 645.7
|
||||
Juliette = 716.1
|
||||
Kilo = 794.3
|
||||
Lima = 881.0
|
||||
Mike = 977.2
|
||||
Papa = 1083.9
|
||||
Quebec = 1202.3
|
||||
Romeo = 1333.5
|
||||
Sierra = 1479.1
|
||||
|
||||
FLT_LEN = 2000 # Samples
|
||||
|
||||
|
||||
# Shamelessly lifted from
|
||||
# https://scipy.github.io/old-wiki/pages/Cookbook/ButterworthBandpass
|
||||
def butter_bandpass(lowcut, highcut, fs, order=5):
|
||||
nyq = 0.5 * fs
|
||||
low = lowcut / nyq
|
||||
high = highcut / nyq
|
||||
b, a = butter(order, [low, high], btype='band')
|
||||
return b, a
|
||||
|
||||
|
||||
def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
|
||||
b, a = butter_bandpass(lowcut, highcut, fs, order=order)
|
||||
y = lfilter(b, a, data)
|
||||
return y
|
||||
|
||||
|
||||
# tone synthesis
|
||||
def note(freq, cycles, amp=32767.0, rate=44100):
|
||||
len = cycles * (1.0/rate)
|
||||
t = np.linspace(0, len, int(len * rate))
|
||||
if freq == 0:
|
||||
data = np.zeros(int(len * rate))
|
||||
else:
|
||||
data = np.sin(2 * np.pi * freq * t) * amp
|
||||
return data.astype(int)
|
||||
|
||||
|
||||
# analyze wav file
|
||||
def analyze(file_name):
|
||||
try:
|
||||
sig_rate, sig_noise = read(file_name)
|
||||
except Exception:
|
||||
print('Error opening {}'.format(file_name))
|
||||
return
|
||||
|
||||
print('file: ', file_name, ' rate: ', sig_rate, ' len: ', len(sig_noise))
|
||||
|
||||
if sig_rate == 44100:
|
||||
decimate = 4 # rate = 11025, Fmax = 5512.5 Hz
|
||||
elif sig_rate == 48000:
|
||||
decimate = 5 # rate = 9600, Fmax = 4800 Hz
|
||||
elif sig_rate == 22050:
|
||||
decimate = 2 # rate = 11025, Fmax = 5512.5 Hz
|
||||
elif sig_rate == 11025:
|
||||
decimate = 1 # rate = 11025, Fmax = 5512.5 Hz
|
||||
else:
|
||||
print('Sample rate {} not supported.'.format(sig_rate))
|
||||
return
|
||||
|
||||
if decimate > 1:
|
||||
sig_noise = signal.decimate(sig_noise, decimate)
|
||||
sig_rate = sig_rate / decimate
|
||||
print('length after decimation: ', len(sig_noise))
|
||||
|
||||
sig_noise = butter_bandpass_filter(sig_noise, 270, 1700, sig_rate, order=8)
|
||||
|
||||
sigA = note(Alpha, FLT_LEN, rate=sig_rate)
|
||||
sigB = note(Bravo, FLT_LEN, rate=sig_rate)
|
||||
sigC = note(Charlie, FLT_LEN, rate=sig_rate)
|
||||
sigD = note(Delta, FLT_LEN, rate=sig_rate)
|
||||
sigE = note(Echo, FLT_LEN, rate=sig_rate)
|
||||
sigF = note(Foxtrot, FLT_LEN, rate=sig_rate)
|
||||
sigG = note(Golf, FLT_LEN, rate=sig_rate)
|
||||
sigH = note(Hotel, FLT_LEN, rate=sig_rate)
|
||||
sigJ = note(Juliette, FLT_LEN, rate=sig_rate)
|
||||
sigK = note(Kilo, FLT_LEN, rate=sig_rate)
|
||||
sigL = note(Lima, FLT_LEN, rate=sig_rate)
|
||||
sigM = note(Mike, FLT_LEN, rate=sig_rate)
|
||||
sigP = note(Papa, FLT_LEN, rate=sig_rate)
|
||||
sigQ = note(Quebec, FLT_LEN, rate=sig_rate)
|
||||
sigR = note(Romeo, FLT_LEN, rate=sig_rate)
|
||||
sigS = note(Sierra, FLT_LEN, rate=sig_rate)
|
||||
|
||||
corrA = np.abs(signal.correlate(sig_noise, sigA, mode='same'))
|
||||
print('A: {}'.format(log10(corrA.sum())))
|
||||
|
||||
corrB = np.abs(signal.correlate(sig_noise, sigB, mode='same'))
|
||||
print('B: {}'.format(log10(corrB.sum())))
|
||||
|
||||
corrC = np.abs(signal.correlate(sig_noise, sigC, mode='same'))
|
||||
print('C: {}'.format(log10(corrC.sum())))
|
||||
|
||||
corrD = np.abs(signal.correlate(sig_noise, sigD, mode='same'))
|
||||
print('D: {}'.format(log10(corrD.sum())))
|
||||
|
||||
corrE = np.abs(signal.correlate(sig_noise, sigE, mode='same'))
|
||||
print('E: {}'.format(log10(corrE.sum())))
|
||||
|
||||
corrF = np.abs(signal.correlate(sig_noise, sigF, mode='same'))
|
||||
print('F: {}'.format(log10(corrF.sum())))
|
||||
|
||||
corrG = np.abs(signal.correlate(sig_noise, sigG, mode='same'))
|
||||
print('G: {}'.format(log10(corrG.sum())))
|
||||
|
||||
corrH = np.abs(signal.correlate(sig_noise, sigH, mode='same'))
|
||||
print('H: {}'.format(log10(corrH.sum())))
|
||||
|
||||
corrJ = np.abs(signal.correlate(sig_noise, sigJ, mode='same'))
|
||||
print('J: {}'.format(log10(corrJ.sum())))
|
||||
|
||||
corrK = np.abs(signal.correlate(sig_noise, sigK, mode='same'))
|
||||
print('K: {}'.format(log10(corrK.sum())))
|
||||
|
||||
corrL = np.abs(signal.correlate(sig_noise, sigL, mode='same'))
|
||||
print('L: {}'.format(log10(corrL.sum())))
|
||||
|
||||
corrM = np.abs(signal.correlate(sig_noise, sigM, mode='same'))
|
||||
print('M: {}'.format(log10(corrM.sum())))
|
||||
|
||||
corrP = np.abs(signal.correlate(sig_noise, sigP, mode='same'))
|
||||
print('P: {}'.format(log10(corrP.sum())))
|
||||
|
||||
corrQ = np.abs(signal.correlate(sig_noise, sigQ, mode='same'))
|
||||
print('Q: {}'.format(log10(corrQ.sum())))
|
||||
|
||||
corrR = np.abs(signal.correlate(sig_noise, sigR, mode='same'))
|
||||
print('R: {}'.format(log10(corrR.sum())))
|
||||
|
||||
corrS = np.abs(signal.correlate(sig_noise, sigS, mode='same'))
|
||||
print('S: {}'.format(log10(corrS.sum())))
|
||||
|
||||
fig, (ax_A, ax_B, ax_C, ax_D, ax_E, ax_F, ax_G, ax_H, ax_J, ax_K,
|
||||
ax_L, ax_M, ax_P, ax_Q, ax_R, ax_S) = plt.subplots(16, 1,
|
||||
sharex=True,
|
||||
sharey=True)
|
||||
|
||||
# ax_sig.plot(sig_noise)
|
||||
# ax_sig.set_title('Signal with noise')
|
||||
# ax_sig.axis('off')
|
||||
# ax_sig.margins(0, 0.1)
|
||||
|
||||
ax_A.plot(corrA)
|
||||
ax_A.axhline(np.average(corrA), ls=':')
|
||||
# ax_A.set_title(label='Alpha')
|
||||
ax_A.axis('off')
|
||||
|
||||
ax_B.plot(corrB)
|
||||
ax_B.axhline(np.average(corrB), ls=':')
|
||||
# ax_B.set_title(label='Bravo')
|
||||
ax_B.axis('off')
|
||||
|
||||
ax_C.plot(corrC)
|
||||
ax_C.axhline(np.average(corrC), ls=':')
|
||||
# ax_C.set_title(label='Charlie')
|
||||
ax_C.axis('off')
|
||||
|
||||
ax_D.plot(corrD)
|
||||
ax_D.axhline(np.average(corrD), ls=':')
|
||||
# ax_D.set_title(label='Delta')
|
||||
ax_D.axis('off')
|
||||
|
||||
ax_E.plot(corrE)
|
||||
ax_E.axhline(np.average(corrE), ls=':')
|
||||
# ax_E.set_title(label='Echo')
|
||||
ax_E.axis('off')
|
||||
|
||||
ax_F.plot(corrF)
|
||||
ax_F.axhline(np.average(corrF), ls=':')
|
||||
# ax_F.set_title(label='Foxtrot')
|
||||
ax_F.axis('off')
|
||||
|
||||
ax_G.plot(corrG)
|
||||
ax_G.axhline(np.average(corrG), ls=':')
|
||||
# ax_G.set_title(label='Golf')
|
||||
ax_G.axis('off')
|
||||
|
||||
ax_H.plot(corrH)
|
||||
ax_H.axhline(np.average(corrH), ls=':')
|
||||
# ax_H.set_title(label='Hotel')
|
||||
ax_H.axis('off')
|
||||
|
||||
ax_J.plot(corrJ)
|
||||
ax_J.axhline(np.average(corrJ), ls=':')
|
||||
# ax_J.set_title(label='Juliette')
|
||||
ax_J.axis('off')
|
||||
|
||||
ax_K.plot(corrK)
|
||||
ax_K.axhline(np.average(corrK), ls=':')
|
||||
# ax_K.set_title(label='Kilo')
|
||||
ax_K.axis('off')
|
||||
|
||||
ax_L.plot(corrL)
|
||||
ax_L.axhline(np.average(corrL), ls=':')
|
||||
# ax_L.set_title(label='Lima')
|
||||
ax_L.axis('off')
|
||||
|
||||
ax_M.plot(corrM)
|
||||
ax_M.axhline(np.average(corrM), ls=':')
|
||||
# ax_M.set_title(label='Mike')
|
||||
ax_M.axis('off')
|
||||
|
||||
ax_P.plot(corrP)
|
||||
ax_P.axhline(np.average(corrP), ls=':')
|
||||
# ax_P.set_title(label='Papa')
|
||||
ax_P.axis('off')
|
||||
|
||||
ax_Q.plot(corrQ)
|
||||
ax_Q.axhline(np.average(corrQ), ls=':')
|
||||
# ax_Q.set_title(label='Quebec')
|
||||
ax_Q.axis('off')
|
||||
|
||||
ax_R.plot(corrR)
|
||||
ax_R.axhline(np.average(corrR), ls=':')
|
||||
# ax_R.set_title(label='Romeo')
|
||||
ax_R.axis('off')
|
||||
|
||||
ax_S.plot(corrS)
|
||||
ax_S.axhline(np.average(corrS), ls=':')
|
||||
# ax_S.set_title(label='Sierra')
|
||||
ax_S.axis('off')
|
||||
|
||||
fig.set_tight_layout(True)
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
analyze(sys.argv[1])
|
||||
@ -1,187 +0,0 @@
|
||||
'''
|
||||
Calculate the optimum bin sizes to fit the selcal tone assignments
|
||||
|
||||
'''
|
||||
from __future__ import print_function
|
||||
import bidict
|
||||
import math
|
||||
|
||||
|
||||
class Tones(object):
|
||||
"""
|
||||
The selcal tone definitions
|
||||
"""
|
||||
RED = bidict.bidict(
|
||||
# Designation: Frequency (Hz)
|
||||
Alpha=312.6,
|
||||
Bravo=346.7,
|
||||
Charlie=384.6,
|
||||
Delta=426.6,
|
||||
Echo=473.2,
|
||||
Foxtrot=524.8,
|
||||
Golf=582.1,
|
||||
Hotel=645.7,
|
||||
Juliet=716.1,
|
||||
Kilo=794.3,
|
||||
Lima=881.0,
|
||||
Mike=977.2,
|
||||
Papa=1083.9,
|
||||
Quebec=1202.3,
|
||||
Romeo=1333.5,
|
||||
Sierra=1479.1,)
|
||||
|
||||
def freq(self, desig):
|
||||
"""
|
||||
:return: The frequency assigned to the given designator
|
||||
"""
|
||||
return Tones.RED[desig]
|
||||
|
||||
def tone(self, freq):
|
||||
"""
|
||||
:return: The designator for the given frequency
|
||||
"""
|
||||
try:
|
||||
return Tones.RED[:freq]
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
class SelcalParams:
|
||||
"""
|
||||
Contains the specifications and parameters for the
|
||||
set of 16 selcal tones. The specified frequencies,
|
||||
tolerances, and associated DFT bins are attributes.
|
||||
|
||||
"""
|
||||
def __init__(self, samplerate):
|
||||
self.samplerate = samplerate * 1.0
|
||||
self.selcal_tones = [312.6, 346.7, 384.6, 426.6,
|
||||
473.2, 524.8, 582.1, 645.7,
|
||||
716.1, 794.3, 881.0, 977.2,
|
||||
1083.9, 1202.3, 1333.5, 1479.1, ]
|
||||
self.log_tone_step = 0.045
|
||||
self.log_tone_tolerance = 0.0015
|
||||
self.tone_lower = {}
|
||||
self.tone_upper = {}
|
||||
self.tone_step = {}
|
||||
self.tone_window = {}
|
||||
self.bin_center = {}
|
||||
self.bin_error = {}
|
||||
|
||||
for tone in range(len(self.selcal_tones)):
|
||||
self.tone_lower[tone] = \
|
||||
self.selcal_tones[tone] * (1 - self.log_tone_tolerance)
|
||||
self.tone_upper[tone] = \
|
||||
self.selcal_tones[tone] * (1 + self.log_tone_tolerance)
|
||||
self.tone_window[tone] = \
|
||||
self.tone_upper[tone] - self.tone_lower[tone]
|
||||
|
||||
for tone in range(12, 28):
|
||||
index = tone - 12
|
||||
lower = math.pow(10, (2.0 + ((tone - 1) * self.log_tone_step)))
|
||||
upper = math.pow(10, (2.0 + (tone * self.log_tone_step)))
|
||||
self.tone_step[index] = (upper - lower)
|
||||
|
||||
def calc_bin_error(self, binsize):
|
||||
"""
|
||||
For a given sample block size, calculate the freqency error between
|
||||
the derived DFT frequency bins and the selcal tones. Return an
|
||||
arbitrary score for the net frequency error across all of the selcal
|
||||
tones. Currently, this is set to a log10 value, so more negative
|
||||
scores are better.
|
||||
"""
|
||||
self.samplesize = binsize
|
||||
self.binsize = (self.samplerate / self.samplesize)
|
||||
self.bintime = (self.samplesize / self.samplerate)
|
||||
avg_err = 0.0
|
||||
for tone in range(len(self.selcal_tones)):
|
||||
self.bin_center[tone] = \
|
||||
((1.0 * int(self.selcal_tones[tone] / self.binsize)) + 0.5) \
|
||||
* self.binsize
|
||||
bin_err = abs(self.selcal_tones[tone] - self.bin_center[tone])
|
||||
self.bin_error[tone] = bin_err
|
||||
if bin_err > 0:
|
||||
avg_err = avg_err + math.log10(bin_err)
|
||||
return(avg_err)
|
||||
|
||||
def print_bin_error(self, samplerate, samplesize):
|
||||
"""
|
||||
Given a sample rate and size, print out the frequency error for
|
||||
each DFT bin versus the selcal tone frequency.
|
||||
|
||||
"""
|
||||
binsize = (samplerate / samplesize)
|
||||
print("=== Rate: ",
|
||||
samplerate,
|
||||
" size : ",
|
||||
samplesize,
|
||||
" time: ",
|
||||
samplesize * 1.0 / samplerate,
|
||||
" ===")
|
||||
for tone in range(len(self.selcal_tones)):
|
||||
self.bin_center[tone] = \
|
||||
((1.0 * int(self.selcal_tones[tone] / binsize)) + 0.5) \
|
||||
* binsize
|
||||
bin_err = self.selcal_tones[tone] - self.bin_center[tone]
|
||||
print("Tone: ",
|
||||
tone,
|
||||
" freq: ",
|
||||
self.selcal_tones[tone],
|
||||
" bin: ",
|
||||
self.bin_center[tone],
|
||||
" err: ",
|
||||
bin_err,
|
||||
" tol: ",
|
||||
self.selcal_tones[tone] * self.log_tone_tolerance)
|
||||
print()
|
||||
|
||||
def search_err(self, upper_bound):
|
||||
"""
|
||||
Search sample sizes starting from a given upper bound to a
|
||||
set lower bound, recursively. Return when the upper bound
|
||||
equals the lower bound.
|
||||
|
||||
"""
|
||||
lower_bound = int(self.samplerate / 40)
|
||||
if upper_bound > lower_bound:
|
||||
min_err = 10000.0
|
||||
min_size = upper_bound
|
||||
for size in range(lower_bound, upper_bound):
|
||||
err = params.calc_bin_error(size)
|
||||
if err < min_err:
|
||||
min_err = err
|
||||
min_size = size
|
||||
|
||||
print("rate: ",
|
||||
rate,
|
||||
" size ",
|
||||
min_size,
|
||||
" time ",
|
||||
(min_size * 1.0 / rate),
|
||||
" error ",
|
||||
min_err)
|
||||
self.search_err(min_size)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
_samplerates = [44100, 32000, 22050, 16000, 11025, 8000, 4000]
|
||||
for rate in _samplerates:
|
||||
params = SelcalParams(rate)
|
||||
upper_bound = int(rate / 9)
|
||||
params.search_err(upper_bound)
|
||||
print()
|
||||
|
||||
# rate: 44100 size 4580 time 0.103854875283 error -3.60366543025
|
||||
params.print_bin_error(44100, 4580)
|
||||
# rate: 32000 size 3553 time 0.11103125 error -3.36174781256
|
||||
params.print_bin_error(32000, 3553)
|
||||
# rate: 22050 size 2290 time 0.103854875283 error -3.60366543025
|
||||
params.print_bin_error(22050, 2290)
|
||||
# rate: 16000 size 1662 time 0.103875 error -2.49558719312
|
||||
params.print_bin_error(16000, 1662)
|
||||
# rate: 11025 size 1145 time 0.103854875283 error -3.60366543025
|
||||
params.print_bin_error(11025, 1145)
|
||||
# rate: 8000 size 831 time 0.103875 error -2.49558719312
|
||||
params.print_bin_error(8000, 831)
|
||||
# rate: 4000 size 361 time 0.09025 error -1.53481794027
|
||||
params.print_bin_error(4000, 361)
|
||||
@ -1,84 +0,0 @@
|
||||
# Run with "ipython -i --matplotlib=qt correlate.py"
|
||||
#
|
||||
from __future__ import print_function
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from scipy import signal
|
||||
import matplotlib.pyplot as plt
|
||||
from scipy.io.wavfile import write
|
||||
|
||||
RATE = 44100
|
||||
Alpha = 312.6
|
||||
|
||||
|
||||
# See http://www.dsprelated.com/showarticle/908.php
|
||||
def voss(nrows, ncols=16):
|
||||
"""Generates pink noise using the Voss-McCartney algorithm.
|
||||
|
||||
nrows: number of values to generate
|
||||
rcols: number of random sources to add
|
||||
|
||||
returns: NumPy array
|
||||
"""
|
||||
array = np.empty((nrows, ncols))
|
||||
array.fill(np.nan)
|
||||
array[0, :] = np.random.random(ncols)
|
||||
array[:, 0] = np.random.random(nrows)
|
||||
|
||||
# the total number of changes is nrows
|
||||
n = nrows
|
||||
cols = np.random.geometric(0.5, n)
|
||||
cols[cols >= ncols] = 0
|
||||
rows = np.random.randint(nrows, size=n)
|
||||
array[rows, cols] = np.random.random(n)
|
||||
|
||||
df = pd.DataFrame(array)
|
||||
df.fillna(method='ffill', axis=0, inplace=True)
|
||||
total = df.sum(axis=1)
|
||||
|
||||
return (total.values - (ncols/2))
|
||||
|
||||
|
||||
# tone synthesis
|
||||
def note(freq, len, amp=1, rate=RATE):
|
||||
t = np.linspace(0, len, int(len * rate))
|
||||
if freq == 0:
|
||||
data = np.zeros(int(len * rate))
|
||||
else:
|
||||
data = np.sin(2*np.pi*freq*t)*amp
|
||||
return data
|
||||
|
||||
|
||||
sig = note(Alpha, 0.10)
|
||||
sig_tx = np.concatenate((note(0, 0.50),
|
||||
note(Alpha, 0.20),
|
||||
note(0, 0.10),
|
||||
note(Alpha, 0.75),
|
||||
note(0, 0.10),
|
||||
note(Alpha, 0.10),
|
||||
note(0, 0.10),))
|
||||
# sig_noise = 0.33 * sig_tx + 0.66 * np.random.randn(len(sig_tx))
|
||||
sig_noise = 0.50 * sig_tx + 0.50 * voss(len(sig_tx))
|
||||
corr = np.abs(signal.correlate(sig_noise, sig, mode='same'))
|
||||
|
||||
fig, (ax_orig, ax_sig, ax_noise, ax_corr) = plt.subplots(4, 1, sharex=True)
|
||||
|
||||
ax_orig.plot(sig)
|
||||
ax_orig.set_title('Reference')
|
||||
|
||||
ax_sig.plot(sig_tx)
|
||||
ax_sig.set_title('Signal without noise')
|
||||
|
||||
ax_noise.plot(sig_noise)
|
||||
ax_noise.set_title('Signal with noise')
|
||||
|
||||
ax_corr.plot(corr)
|
||||
ax_corr.axhline(np.average(corr), ls=':')
|
||||
ax_corr.set_title('Cross-correlated with Reference')
|
||||
|
||||
ax_orig.margins(0, 0.1)
|
||||
|
||||
fig.set_tight_layout(True)
|
||||
plt.show()
|
||||
|
||||
write('test.wav', 44100, sig_noise)
|
||||
@ -1,47 +0,0 @@
|
||||
# Run with "ipython -i --matplotlib=qt match_frequency.py"
|
||||
#
|
||||
from __future__ import print_function
|
||||
import numpy as np
|
||||
# import pandas as pd
|
||||
from scipy import signal
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
RATE = 44100
|
||||
|
||||
|
||||
# tone synthesis
|
||||
def tone(freq, cycles, amp=1, rate=RATE):
|
||||
len = cycles * (1.0/rate)
|
||||
t = np.linspace(0, len, int(len * rate))
|
||||
if freq == 0:
|
||||
data = np.zeros(int(len * rate))
|
||||
else:
|
||||
data = np.sin(2 * np.pi * freq * t) * amp
|
||||
return data
|
||||
|
||||
|
||||
fig, ax = plt.subplots(1, 1, sharex=True)
|
||||
|
||||
for carrier in [312, 473, 716, 1084, 1479]:
|
||||
print("carrier = ", carrier)
|
||||
|
||||
freqs = range(carrier-20, carrier+22, 2)
|
||||
sig = tone(carrier * 1.0, 2000) # Reference tone
|
||||
response = []
|
||||
|
||||
for freq in freqs:
|
||||
sig_tx = tone(freq * 1.0, 2000) # Test tone
|
||||
resp = np.abs(signal.correlate(sig, sig_tx, mode='same'))
|
||||
response.append(resp.sum())
|
||||
|
||||
ax.semilogy(freqs, response, label='tone = {}'.format(carrier))
|
||||
ax.set_title('Matched filter response')
|
||||
|
||||
ax.axvline(626.67, ls=':') # Guardband markers
|
||||
ax.axvline(695.01, ls=':')
|
||||
|
||||
ax.legend(loc='best')
|
||||
ax.margins(0, 0.1)
|
||||
|
||||
fig.set_tight_layout(True)
|
||||
plt.show()
|
||||
@ -1,48 +0,0 @@
|
||||
# Run with "ipython -i --matplotlib=qt correlate.py"
|
||||
#
|
||||
from __future__ import print_function
|
||||
import numpy as np
|
||||
# import pandas as pd
|
||||
from scipy import signal
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
RATE = 44100
|
||||
|
||||
|
||||
# tone synthesis
|
||||
def tone(freq, cycles, amp=1, rate=RATE):
|
||||
len = cycles * (1.0/freq)
|
||||
t = np.linspace(0, len, len * rate)
|
||||
if freq is 0:
|
||||
data = np.zeros(int(len * rate))
|
||||
else:
|
||||
data = np.sin(2 * np.pi * freq * t) * amp
|
||||
return data
|
||||
|
||||
|
||||
freqs = range(600, 722, 2) # Frequency range
|
||||
|
||||
fig, ax = plt.subplots(1, 1, sharex=True)
|
||||
|
||||
for length in [32, 64, 100, 128, 256]:
|
||||
print("len = ", length)
|
||||
|
||||
sig = tone(660.0, length) # Reference tone
|
||||
response = []
|
||||
|
||||
for freq in freqs:
|
||||
sig_tx = tone(freq * 1.0, length) # Test tone
|
||||
resp = np.abs(signal.correlate(sig, sig_tx, mode='same'))
|
||||
response.append(resp.sum())
|
||||
|
||||
ax.semilogy(freqs, response, label='len = {}'.format(length))
|
||||
ax.set_title('Matched filter response')
|
||||
|
||||
ax.axvline(626.67, ls=':') # Guardband markers
|
||||
ax.axvline(695.01, ls=':')
|
||||
|
||||
ax.legend(loc='best')
|
||||
ax.margins(0, 0.1)
|
||||
|
||||
fig.set_tight_layout(True)
|
||||
fig.show()
|
||||
@ -1,190 +0,0 @@
|
||||
# Run with "ipython --matplotlib=qt receiver.py <file>.wav"
|
||||
#
|
||||
from __future__ import print_function
|
||||
import sys
|
||||
import numpy as np
|
||||
from scipy import signal
|
||||
from scipy.io.wavfile import read
|
||||
from scipy.signal import butter, lfilter
|
||||
from math import log10
|
||||
|
||||
from matplotlib import pyplot as plt
|
||||
from mpl_toolkits.mplot3d import Axes3D
|
||||
|
||||
FRAME_TIME = 0.1 # Frame time in seconds
|
||||
|
||||
TONES = [312.6,
|
||||
346.7,
|
||||
384.6,
|
||||
426.6,
|
||||
473.2,
|
||||
524.8,
|
||||
582.1,
|
||||
645.7,
|
||||
716.1,
|
||||
794.3,
|
||||
881.0,
|
||||
977.2,
|
||||
1083.9,
|
||||
1202.3,
|
||||
1333.5,
|
||||
1479.1]
|
||||
|
||||
ALPHABET = ['Alpha',
|
||||
'Bravo',
|
||||
'Charlie',
|
||||
'Delta',
|
||||
'Echo',
|
||||
'Foxtrot',
|
||||
'Golf',
|
||||
'Hotel',
|
||||
'Juliette',
|
||||
'Kilo',
|
||||
'Lima',
|
||||
'Mike',
|
||||
'Papa',
|
||||
'Quebec',
|
||||
'Romeo',
|
||||
'Sierra']
|
||||
|
||||
FILTER_LEN = 1000 # Samples
|
||||
|
||||
|
||||
# Shamelessly lifted from
|
||||
# https://scipy.github.io/old-wiki/pages/Cookbook/ButterworthBandpass
|
||||
def butter_bandpass(lowcut, highcut, fs, order=5):
|
||||
nyq = 0.5 * fs
|
||||
low = lowcut / nyq
|
||||
high = highcut / nyq
|
||||
b, a = butter(order, [low, high], btype='band')
|
||||
return b, a
|
||||
|
||||
|
||||
def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
|
||||
b, a = butter_bandpass(lowcut, highcut, fs, order=order)
|
||||
y = lfilter(b, a, data)
|
||||
return y
|
||||
|
||||
|
||||
# tone synthesis
|
||||
def note(freq, cycles, amp=32767.0, rate=44100):
|
||||
len = cycles * (1.0/rate)
|
||||
t = np.linspace(0, len, int(len * rate))
|
||||
if freq == 0:
|
||||
data = np.zeros(int(len * rate))
|
||||
else:
|
||||
data = np.sin(2 * np.pi * freq * t) * amp
|
||||
return data.astype(int)
|
||||
|
||||
|
||||
# analyze wav file by chunks
|
||||
def receiver(file_name):
|
||||
try:
|
||||
sig_rate, sig_noise = read(file_name)
|
||||
except Exception:
|
||||
print('Error opening {}'.format(file_name))
|
||||
return
|
||||
|
||||
print('file: ', file_name, ' rate: ', sig_rate, ' len: ', len(sig_noise))
|
||||
|
||||
if sig_rate == 44100:
|
||||
decimate = 4 # rate = 11025, Fmax = 5512.5 Hz
|
||||
elif sig_rate == 48000:
|
||||
decimate = 5 # rate = 9600, Fmax = 4800 Hz
|
||||
elif sig_rate == 22050:
|
||||
decimate = 2 # rate = 11025, Fmax = 5512.5 Hz
|
||||
elif sig_rate == 11025:
|
||||
decimate = 1 # rate = 11025, Fmax = 5512.5 Hz
|
||||
else:
|
||||
print('Sample rate {} not supported.'.format(sig_rate))
|
||||
return
|
||||
|
||||
if decimate > 1:
|
||||
sig_noise = signal.decimate(sig_noise, decimate)
|
||||
sig_rate = sig_rate / decimate
|
||||
print('length after decimation: ', len(sig_noise))
|
||||
|
||||
frame_len = int(sig_rate * FRAME_TIME)
|
||||
frames = int((len(sig_noise) / frame_len) + 1)
|
||||
|
||||
sig_noise = butter_bandpass_filter(sig_noise,
|
||||
270,
|
||||
1700,
|
||||
sig_rate,
|
||||
order=8)
|
||||
|
||||
template = []
|
||||
for tone in range(0, len(TONES)):
|
||||
template.append(note(TONES[tone], frame_len, rate=sig_rate))
|
||||
|
||||
# See http://stackoverflow.com/questions/23507217/
|
||||
# python-plotting-2d-data-on-to-3d-axes
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(111, projection='3d')
|
||||
|
||||
y = np.arange(len(TONES))
|
||||
print(' Index A B C D E F G', end='')
|
||||
print(' H J K L M P Q R', end='')
|
||||
print(' S Avg')
|
||||
|
||||
x = range(0, frames)
|
||||
X, Y = np.meshgrid(y, x)
|
||||
Z = np.zeros((len(x), len(y)))
|
||||
|
||||
for frame in range(0, frames):
|
||||
|
||||
beg = frame * frame_len
|
||||
end = (frame+1) * frame_len
|
||||
|
||||
corr = np.zeros(len(TONES))
|
||||
|
||||
for tone in range(0, len(TONES)):
|
||||
corr[tone] = log10(np.abs(signal.correlate(sig_noise[beg:end],
|
||||
template[tone],
|
||||
mode='same')).sum())
|
||||
Z[frame, tone] = corr[tone]
|
||||
|
||||
max1 = 0.0
|
||||
for tone in range(0, len(TONES)):
|
||||
if corr[tone] > max1:
|
||||
max1 = corr[tone]
|
||||
max1idx = tone
|
||||
|
||||
max2 = 0.0
|
||||
for tone in range(0, len(TONES)):
|
||||
if tone != max1idx and corr[tone] > max2:
|
||||
max2 = corr[tone]
|
||||
max2idx = tone
|
||||
|
||||
print('{0:6d}: '.format(frame), end='')
|
||||
avg = np.mean(corr)
|
||||
for tone in range(0, len(TONES)):
|
||||
if tone == max1idx or tone == max2idx:
|
||||
print('[{0:2.2f}]'.format(corr[tone]), end='')
|
||||
else:
|
||||
if corr[tone] > avg:
|
||||
print(' {0:2.2f} '.format(corr[tone]), end='')
|
||||
else:
|
||||
print(' . ', end='')
|
||||
|
||||
print(' {0:2.2f}'.format(avg))
|
||||
|
||||
ax.plot_surface(X, Y, Z, rstride=1, cstride=1000, color='w', shade=True,
|
||||
lw=.5)
|
||||
# ax.plot_wireframe(X, Y, Z, rstride=1, cstride=1000, lw=.5)
|
||||
|
||||
ax.set_title(file_name)
|
||||
ax.set_xlabel("Tone")
|
||||
ax.set_ylabel("Frame")
|
||||
ax.set_zlabel("Log Correlation")
|
||||
|
||||
ax.set_zlim(10.0, 15.0)
|
||||
ax.set_ylim(0, frames)
|
||||
|
||||
ax.view_init(30, -130)
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
receiver(sys.argv[1])
|
||||
@ -1,97 +0,0 @@
|
||||
# Run with "ipython -i --matplotlib=qt spectrum.py <file>.wav"
|
||||
#
|
||||
from __future__ import print_function
|
||||
import sys
|
||||
import numpy as np
|
||||
# import pandas as pd
|
||||
from scipy import signal
|
||||
import matplotlib.pyplot as plt
|
||||
from scipy.io.wavfile import read
|
||||
from scipy.signal import butter, lfilter
|
||||
|
||||
Tones = dict({'Alpha': 312.6,
|
||||
'Bravo': 346.7,
|
||||
'Charlie': 384.6,
|
||||
'Delta': 426.6,
|
||||
'Echo': 473.2,
|
||||
'Foxtrot': 524.8,
|
||||
'Golf': 582.1,
|
||||
'Hotel': 645.7,
|
||||
'Juliette': 716.1,
|
||||
'Kilo': 794.3,
|
||||
'Lima': 881.0,
|
||||
'Mike': 977.2,
|
||||
'Papa': 1083.9,
|
||||
'Quebec': 1202.3,
|
||||
'Romeo': 1333.5,
|
||||
'Sierra': 1479.1, })
|
||||
|
||||
|
||||
# Shamelessly lifted from
|
||||
# https://scipy.github.io/old-wiki/pages/Cookbook/ButterworthBandpass
|
||||
def butter_bandpass(lowcut, highcut, fs, order=5):
|
||||
nyq = 0.5 * fs
|
||||
low = lowcut / nyq
|
||||
high = highcut / nyq
|
||||
b, a = butter(order, [low, high], btype='band')
|
||||
return b, a
|
||||
|
||||
|
||||
def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
|
||||
b, a = butter_bandpass(lowcut, highcut, fs, order=order)
|
||||
y = lfilter(b, a, data)
|
||||
return y
|
||||
|
||||
|
||||
# wav file spectrum
|
||||
def spectrum(file_name):
|
||||
try:
|
||||
sig_rate, sig_noise = read(file_name)
|
||||
except Exception:
|
||||
print('Error opening {}'.format(file_name))
|
||||
return
|
||||
|
||||
print('file: ', file_name, ' rate: ', sig_rate, ' len: ', len(sig_noise))
|
||||
|
||||
if sig_rate == 44100:
|
||||
decimate = 12 # rate = 3675, Fmax = 1837.5 Hz
|
||||
elif sig_rate == 48000:
|
||||
decimate = 10 # rate = 4800, Fmax = 2400 Hz
|
||||
elif sig_rate == 22050:
|
||||
decimate = 6 # rate = 3675, Fmax = 1837.5 Hz
|
||||
elif sig_rate == 11025:
|
||||
decimate = 3 # rate = 3675, Fmax = 1837.5 Hz
|
||||
else:
|
||||
print('Sample rate {} not supported.'.format(sig_rate))
|
||||
return
|
||||
|
||||
if decimate > 1:
|
||||
sig_noise = signal.decimate(sig_noise, decimate)
|
||||
sig_rate = sig_rate/decimate
|
||||
print('length after decimation: ', len(sig_noise))
|
||||
|
||||
sig_noise = butter_bandpass_filter(sig_noise, 270, 1700, sig_rate, order=8)
|
||||
|
||||
sig_f, welch_spec = signal.welch(sig_noise, sig_rate, nperseg=2048,
|
||||
nfft=65536, scaling='spectrum')
|
||||
|
||||
plt.title(file_name)
|
||||
plt.xlabel('frequency [Hz]')
|
||||
plt.ylabel('PSD')
|
||||
plt.xlim(270, 1700)
|
||||
plt.ylim([1e-2, 1e7])
|
||||
for tone in Tones:
|
||||
plt.axvline(Tones[tone], color='g')
|
||||
plt.axvspan(Tones[tone]*0.98,
|
||||
Tones[tone]*1.02, facecolor='r', alpha=0.5)
|
||||
plt.grid()
|
||||
|
||||
# plt.semilogy(sig_f, welch_spec)
|
||||
plt.loglog(sig_f, welch_spec)
|
||||
plt.axhline(np.average(welch_spec), color='r')
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
spectrum(sys.argv[1])
|
||||
@ -1,33 +0,0 @@
|
||||
"""Inspired by http://stackoverflow.com/questions/24974032/reading-realtime
|
||||
-audio-data-into-numpy-array."""
|
||||
from __future__ import print_function
|
||||
import pyaudio
|
||||
import numpy
|
||||
import scipy.io.wavefile as wav
|
||||
|
||||
RATE = 16000
|
||||
RECORD_SECONDS = 2.5
|
||||
CHUNKSIZE = 1024
|
||||
|
||||
# initialize portaudio
|
||||
p = pyaudio.PyAudio()
|
||||
stream = p.open(format=pyaudio.paInt16,
|
||||
channels=1,
|
||||
rate=RATE,
|
||||
input=True,
|
||||
frames_per_buffer=CHUNKSIZE)
|
||||
|
||||
frames = [] # A python-list of chunks(numpy.ndarray)
|
||||
for _ in range(0, int(RATE / CHUNKSIZE * RECORD_SECONDS)):
|
||||
data = stream.read(CHUNKSIZE)
|
||||
frames.append(numpy.fromstring(data, dtype=numpy.int16))
|
||||
|
||||
# Convert the list of numpy-arrays into a 1D array (column-wise)
|
||||
numpydata = numpy.hstack(frames)
|
||||
|
||||
# close stream
|
||||
stream.stop_stream()
|
||||
stream.close()
|
||||
p.terminate()
|
||||
|
||||
wav.write('out.wav', RATE, numpydata)
|
||||
Loading…
Reference in New Issue
Block a user