![]() ![]() The lower the threshold_softness, the more extreme the noise detection becomes'''Īssert stat_mode in, print(f"expected 'mean', 'median' or 'mode' for `stat_mode` but received: ' segments") ![]() If the segments have a more than average amplitude /threshold_softness then it probably is actual part of the song This method is effective as it looks at the local area and search for the noise Then it mens those areas are probably only noise and therefore the middle segment will also become silence It checks the previous segment and the next segment of the current segment and if those segments have a lower than average amplitude / threshold_softness '''This method runs a window on the wav signal. Return amplitude_envelope, instantaneous_frequencyĭef denoise(wav_file_handler, hop_length:int=1024, window_length_in_second:float=0.5, threshold_softness:float=4.0, stat_mode="mean", verbose:int=0)->np.array: Instantaneous_frequency += np.max(instantaneous_frequency) Instantaneous_frequency = (np.diff(instantaneous_phase) / Instantaneous_phase = np.unwrap(np.angle(analytic_signal)) ![]() '''this calculates the amplitude envelope of the audio and returns it'''Īnalytic_signal = sp.signal.hilbert(signal)Īmplitude_envelope = np.abs(analytic_signal) I used the accepted answer suggestion and created the following algorithm which uses the Hilbert envelope and denoises parts of the song when there is a noise with no vocals. ![]()
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