| import torch | |
| import torch.nn.functional as F | |
| from torch import nn | |
| from ...core import AudioSignal | |
| from ...core import STFTParams | |
| from ...core import util | |
| class SpectralGate(nn.Module): | |
| """Spectral gating algorithm for noise reduction, | |
| as in Audacity/Ocenaudio. The steps are as follows: | |
| 1. An FFT is calculated over the noise audio clip | |
| 2. Statistics are calculated over FFT of the the noise | |
| (in frequency) | |
| 3. A threshold is calculated based upon the statistics | |
| of the noise (and the desired sensitivity of the algorithm) | |
| 4. An FFT is calculated over the signal | |
| 5. A mask is determined by comparing the signal FFT to the | |
| threshold | |
| 6. The mask is smoothed with a filter over frequency and time | |
| 7. The mask is appled to the FFT of the signal, and is inverted | |
| Implementation inspired by Tim Sainburg's noisereduce: | |
| https://timsainburg.com/noise-reduction-python.html | |
| Parameters | |
| ---------- | |
| n_freq : int, optional | |
| Number of frequency bins to smooth by, by default 3 | |
| n_time : int, optional | |
| Number of time bins to smooth by, by default 5 | |
| """ | |
| def __init__(self, n_freq: int = 3, n_time: int = 5): | |
| super().__init__() | |
| smoothing_filter = torch.outer( | |
| torch.cat( | |
| [ | |
| torch.linspace(0, 1, n_freq + 2)[:-1], | |
| torch.linspace(1, 0, n_freq + 2), | |
| ] | |
| )[..., 1:-1], | |
| torch.cat( | |
| [ | |
| torch.linspace(0, 1, n_time + 2)[:-1], | |
| torch.linspace(1, 0, n_time + 2), | |
| ] | |
| )[..., 1:-1], | |
| ) | |
| smoothing_filter = smoothing_filter / smoothing_filter.sum() | |
| smoothing_filter = smoothing_filter.unsqueeze(0).unsqueeze(0) | |
| self.register_buffer("smoothing_filter", smoothing_filter) | |
| def forward( | |
| self, | |
| audio_signal: AudioSignal, | |
| nz_signal: AudioSignal, | |
| denoise_amount: float = 1.0, | |
| n_std: float = 3.0, | |
| win_length: int = 2048, | |
| hop_length: int = 512, | |
| ): | |
| """Perform noise reduction. | |
| Parameters | |
| ---------- | |
| audio_signal : AudioSignal | |
| Audio signal that noise will be removed from. | |
| nz_signal : AudioSignal, optional | |
| Noise signal to compute noise statistics from. | |
| denoise_amount : float, optional | |
| Amount to denoise by, by default 1.0 | |
| n_std : float, optional | |
| Number of standard deviations above which to consider | |
| noise, by default 3.0 | |
| win_length : int, optional | |
| Length of window for STFT, by default 2048 | |
| hop_length : int, optional | |
| Hop length for STFT, by default 512 | |
| Returns | |
| ------- | |
| AudioSignal | |
| Denoised audio signal. | |
| """ | |
| stft_params = STFTParams(win_length, hop_length, "sqrt_hann") | |
| audio_signal = audio_signal.clone() | |
| audio_signal.stft_data = None | |
| audio_signal.stft_params = stft_params | |
| nz_signal = nz_signal.clone() | |
| nz_signal.stft_params = stft_params | |
| nz_stft_db = 20 * nz_signal.magnitude.clamp(1e-4).log10() | |
| nz_freq_mean = nz_stft_db.mean(keepdim=True, dim=-1) | |
| nz_freq_std = nz_stft_db.std(keepdim=True, dim=-1) | |
| nz_thresh = nz_freq_mean + nz_freq_std * n_std | |
| stft_db = 20 * audio_signal.magnitude.clamp(1e-4).log10() | |
| nb, nac, nf, nt = stft_db.shape | |
| db_thresh = nz_thresh.expand(nb, nac, -1, nt) | |
| stft_mask = (stft_db < db_thresh).float() | |
| shape = stft_mask.shape | |
| stft_mask = stft_mask.reshape(nb * nac, 1, nf, nt) | |
| pad_tuple = ( | |
| self.smoothing_filter.shape[-2] // 2, | |
| self.smoothing_filter.shape[-1] // 2, | |
| ) | |
| stft_mask = F.conv2d(stft_mask, self.smoothing_filter, padding=pad_tuple) | |
| stft_mask = stft_mask.reshape(*shape) | |
| stft_mask *= util.ensure_tensor(denoise_amount, ndim=stft_mask.ndim).to( | |
| audio_signal.device | |
| ) | |
| stft_mask = 1 - stft_mask | |
| audio_signal.stft_data *= stft_mask | |
| audio_signal.istft() | |
| return audio_signal | |