def test_lowpass(self):
"""
Test biquad lowpass filter, compare to SoX implementation
"""
cutoff_freq = 3000
noise_filepath = common_utils.get_asset_path('whitenoise.wav')
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("lowpass", [cutoff_freq])
sox_output_waveform, sr = E.sox_build_flow_effects()
waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True)
output_waveform = F.lowpass_biquad(waveform, sample_rate, cutoff_freq)
self.assertEqual(output_waveform, sox_output_waveform, atol=1e-4, rtol=1e-5)
def test_lowpass(self):
"""
Test biquad lowpass filter, compare to SoX implementation
"""
cutoff_freq = 3000
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(self.noise_filepath)
E.append_effect_to_chain("lowpass", [cutoff_freq])
sox_output_waveform, sr = E.sox_build_flow_effects()
output_waveform = F.lowpass_biquad(self.noise_waveform,
self.NOISE_SAMPLE_RATE, cutoff_freq)
self.assertEqual(output_waveform,
sox_output_waveform,
atol=1e-4,
rtol=1e-5)
def test_lowpass(self):
"""
Test biquad lowpass filter, compare to SoX implementation
"""
CUTOFF_FREQ = 3000
noise_filepath = os.path.join(self.test_dirpath, "assets", "whitenoise.mp3")
E = torchaudio.sox_effects.SoxEffectsChain()
E.set_input_file(noise_filepath)
E.append_effect_to_chain("lowpass", [CUTOFF_FREQ])
sox_output_waveform, sr = E.sox_build_flow_effects()
waveform, sample_rate = torchaudio.load(noise_filepath, normalization=True)
output_waveform = F.lowpass_biquad(waveform, sample_rate, CUTOFF_FREQ)
assert torch.allclose(sox_output_waveform, output_waveform, atol=1e-4)
def func(tensor):
sample_rate = 44100
cutoff_freq = 3000.
return F.lowpass_biquad(tensor, sample_rate, cutoff_freq)
Min of gain_waveform: {gain_waveform.min()}
Max of gain_waveform: {gain_waveform.max()}
Mean of gain_waveform: {gain_waveform.mean()}
''')
dither_waveform = F.dither(waveform)
print(f'''
Min of dither_waveform: {dither_waveform.min()}
Max of dither_waveform: {dither_waveform.max()}
Mean of dither_waveform: {dither_waveform.mean()}
''')
# %%
#Applying filters
lowpass_waveform = F.lowpass_biquad(waveform, sample_rate, cutoff_freq=3000)
print(f'''
Min of lowpass_waveform: {lowpass_waveform.min()}
Max of lowpass_waveform: {lowpass_waveform.max()}
Mean of lowpass_waveform: {lowpass_waveform.mean()}
''')
fig, axis = plt.subplots(2)
axis[0].plot(lowpass_waveform[0].t().numpy())
axis[0].set_ylabel("R")
axis[1].plot(lowpass_waveform[1].t().numpy())
axis[1].set_ylabel("L")
highpass_waveform = F.lowpass_biquad(waveform, sample_rate, cutoff_freq=2000)
