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