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import numpy as np
import scipy.signal as signal
from rtlsdr import RtlSdr
class NumpyFmDemod():
'''
Numpy-based FM signal demodulation
Based on the great tutorial by Fraida Fund
https://witestlab.poly.edu/blog/capture-and-decode-fm-radio/
'''
def __init__(self, frequency, sample_rate=1140000, sample_count=8192000, dc_offset=250000):
self.freq = int(frequency)
self.sample_rate = sample_rate
self.sample_count = sample_count
self.dc_offset = dc_offset
def capture_samples(self):
sdr = RtlSdr()
sdr.sample_rate = self.sample_rate
sdr.center_freq = self.freq - self.dc_offset
sdr.gain = 'auto'
self.samples = sdr.read_samples(self.sample_count)
sdr.close()
def load_samples(self, filename):
with open(filename, 'rb') as f:
self.samples = f.read()
def samples_to_np(self):
'''
Convert samples to a complex numpy array
'''
self.samples = np.array(self.samples).astype('complex64')
def mix_down_dc_offset(self):
'''
Mix the samples back down to account for DC offset
'''
fc1 = np.exp(-1.0j * 2.0 * np.pi * self.dc_offset / self.sample_rate * np.arange(len(self.samples)))
self.samples *= fc1
def lowpass_filter(self):
'''
Apply low-pass filter to catch only the target frequency
'''
BANDWIDTH = 200000 # wideband FM signal is always 200kHz
decimation_rate = int(self.sample_rate / BANDWIDTH)
self.samples = signal.decimate(self.samples, decimation_rate)
self.sample_rate /= decimation_rate
def polar_discriminator(self):
'''
Apply a polar discriminator to demodulate the FM signal
'''
self.samples = np.angle(self.samples[1:] * np.conj(self.samples[:-1]))
def de_emphasis_filter(self):
'''
Apply a de-emphasis filter
Still need to figure out exactly what's going on here
'''
d = self.sample_rate * 75e-6
x = np.exp(-1/d)
b, a = [1-x], [1,-x]
self.samples = signal.lfilter(b, a, self.samples)
def mono_decimate(self):
'''
Decimate the signal to catch the mono transmission
'''
audio_freq = 44100.0
decimation_rate = int(self.sample_rate / audio_freq)
self.samples = signal.decimate(self.samples, decimation_rate)
self.sample_rate /= decimation_rate
def scale_volume(self):
'''
Scale samples to adjust volume
'''
self.samples *= 10000 / np.max(np.abs(self.samples))
def output_file(self, filename):
self.samples.astype('int16').tofile(filename)
def demod(self):
self.samples_to_np()
self.mix_down_dc_offset()
self.lowpass_filter()
self.polar_discriminator()
self.de_emphasis_filter()
self.mono_decimate()
self.scale_volume()
if __name__ == '__main__':
nfd = NumpyFmDemod(frequency=96.3e6)
nfd.capture_samples()
#nfd.load_samples('963fm.out')
nfd.demod()
nfd.output_file('wbfm-mono.raw')
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