def reduce_noise( audio_clip, noise_clip, n_grad_freq=2, n_grad_time=4, n_fft=2048, win_length=2048, hop_length=512, n_std_thresh=1.5, prop_decrease=1.0, pad_clipping=True, use_tensorflow=False, verbose=False, ): """Remove noise from audio based upon a clip containing only noise Args: audio_clip (array): The first parameter. noise_clip (array): The second parameter. n_grad_freq (int): how many frequency channels to smooth over with the mask. n_grad_time (int): how many time channels to smooth over with the mask. n_fft (int): number audio of frames between STFT columns. win_length (int): Each frame of audio is windowed by `window()`. The window will be of length `win_length` and then padded with zeros to match `n_fft`.. hop_length (int):number audio of frames between STFT columns. n_std_thresh (int): how many standard deviations louder than the mean dB of the noise (at each frequency level) to be considered signal prop_decrease (float): To what extent should you decrease noise (1 = all, 0 = none) pad_clipping (bool): Pad the signals with zeros to ensure that the reconstructed data is equal length to the data use_tensorflow (bool): Use tensorflow as a backend for convolution and fft to speed up computation verbose (bool): Whether to plot the steps of the algorithm Returns: array: The recovered signal with noise subtracted """ # load tensorflow if you are using it as a backend if use_tensorflow: use_tensorflow = load_tensorflow(verbose) if verbose: pbar = tqdm(total=7) else: pbar = None update_pbar(pbar, "STFT on noise") # STFT over noise noise_stft = _stft(noise_clip, n_fft, hop_length, win_length, use_tensorflow=use_tensorflow) noise_stft_db = _amp_to_db(np.abs(noise_stft)) # convert to dB # Calculate statistics over noise update_pbar(pbar, "STFT on signal") mean_freq_noise = np.mean(noise_stft_db, axis=1) std_freq_noise = np.std(noise_stft_db, axis=1) noise_thresh = mean_freq_noise + std_freq_noise * n_std_thresh # STFT over signal update_pbar(pbar, "STFT on signal") # pad signal with zeros to avoid extra frames being clipped if desired if pad_clipping: nsamp = len(audio_clip) audio_clip = np.pad(audio_clip, [0, hop_length], mode="constant") sig_stft = _stft(audio_clip, n_fft, hop_length, win_length, use_tensorflow=use_tensorflow) sig_stft_db = _amp_to_db(np.abs(sig_stft)) update_pbar(pbar, "Generate mask") # Calculate value to mask dB to mask_gain_dB = np.min(_amp_to_db(np.abs(sig_stft))) # calculate the threshold for each frequency/time bin db_thresh = np.repeat( np.reshape(noise_thresh, [1, len(mean_freq_noise)]), np.shape(sig_stft_db)[1], axis=0, ).T # mask if the signal is above the threshold sig_mask = sig_stft_db < db_thresh update_pbar(pbar, "Smooth mask") # Create a smoothing filter for the mask in time and frequency smoothing_filter = _smoothing_filter(n_grad_freq, n_grad_time) # convolve the mask with a smoothing filter sig_mask = convolve_gaussian(sig_mask, smoothing_filter, use_tensorflow) sig_mask = scipy.signal.fftconvolve(sig_mask, smoothing_filter, mode="same") sig_mask = sig_mask * prop_decrease update_pbar(pbar, "Apply mask") # mask the signal sig_stft_amp, sig_stft_db_masked = mask_signal(sig_stft_db, sig_mask, mask_gain_dB, sig_stft) update_pbar(pbar, "Recover signal") # recover the signal recovered_signal = _istft(sig_stft_amp, n_fft, hop_length, win_length, use_tensorflow=use_tensorflow) # fix the recovered signal length if padding signal if pad_clipping: recovered_signal = librosa.util.fix_length(recovered_signal, nsamp) recovered_spec = _amp_to_db( np.abs( _stft( recovered_signal, n_fft, hop_length, win_length, use_tensorflow=use_tensorflow, ))) if verbose: plot_reduction_steps( noise_stft_db, mean_freq_noise, std_freq_noise, noise_thresh, smoothing_filter, sig_stft_db, sig_mask, sig_stft_db_masked, recovered_spec, ) return recovered_signal
def doit(audio_clip, noise_stft, n_grad_freq, n_grad_time, n_fft, win_length, hop_length, n_std_thresh, prop_decrease, pad_clipping, use_tensorflow, verbose, pbar): noise_stft_db = _amp_to_db(np.abs(noise_stft)) # convert to dB # Calculate statistics over noise update_pbar(pbar, "STFT on noise") mean_freq_noise = np.mean(noise_stft_db, axis=1) std_freq_noise = np.std(noise_stft_db, axis=1) noise_thresh = mean_freq_noise + std_freq_noise * n_std_thresh # STFT over signal update_pbar(pbar, "STFT on signal") # pad signal with zeros to avoid extra frames being clipped if desired if pad_clipping: nsamp = len(audio_clip) audio_clip = np.pad(audio_clip, [0, hop_length], mode="constant") sig_stft = _stft( audio_clip, n_fft, hop_length, win_length, use_tensorflow=use_tensorflow ) # spectrogram of signal in dB sig_stft_db = _amp_to_db(np.abs(sig_stft)) update_pbar(pbar, "Generate mask") # calculate the threshold for each frequency/time bin db_thresh = np.repeat( np.reshape(noise_thresh, [1, len(mean_freq_noise)]), np.shape(sig_stft_db)[1], axis=0, ).T # mask if the signal is above the threshold sig_mask = sig_stft_db < db_thresh update_pbar(pbar, "Smooth mask") # Create a smoothing filter for the mask in time and frequency smoothing_filter = _smoothing_filter(n_grad_freq, n_grad_time) # convolve the mask with a smoothing filter sig_mask = convolve_gaussian(sig_mask, smoothing_filter, use_tensorflow) sig_mask = sig_mask * prop_decrease update_pbar(pbar, "Apply mask") # mask the signal sig_stft_amp = mask_signal(sig_stft, sig_mask) update_pbar(pbar, "Recover signal") # recover the signal recovered_signal = _istft( sig_stft_amp, n_fft, hop_length, win_length, use_tensorflow=use_tensorflow ) # fix the recovered signal length if padding signal if pad_clipping: recovered_signal = librosa.util.fix_length(recovered_signal, nsamp) recovered_spec = _amp_to_db( np.abs( _stft( recovered_signal, n_fft, hop_length, win_length, use_tensorflow=use_tensorflow, ) ) ) if verbose: plot_reduction_steps( noise_stft_db, mean_freq_noise, std_freq_noise, noise_thresh, smoothing_filter, sig_stft_db, sig_mask, recovered_spec, ) return recovered_signal