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93 lines
2.5 KiB
93 lines
2.5 KiB
import torch
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import numpy as np
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from scipy.signal import get_window
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import librosa.util as librosa_util
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def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
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n_fft=800, dtype=np.float32, norm=None):
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"""
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# from librosa 0.6
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Compute the sum-square envelope of a window function at a given hop length.
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This is used to estimate modulation effects induced by windowing
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observations in short-time fourier transforms.
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Parameters
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----------
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window : string, tuple, number, callable, or list-like
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Window specification, as in `get_window`
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n_frames : int > 0
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The number of analysis frames
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hop_length : int > 0
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The number of samples to advance between frames
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win_length : [optional]
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The length of the window function. By default, this matches `n_fft`.
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n_fft : int > 0
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The length of each analysis frame.
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dtype : np.dtype
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The data type of the output
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Returns
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-------
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wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
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The sum-squared envelope of the window function
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"""
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if win_length is None:
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win_length = n_fft
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n = n_fft + hop_length * (n_frames - 1)
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x = np.zeros(n, dtype=dtype)
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# Compute the squared window at the desired length
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win_sq = get_window(window, win_length, fftbins=True)
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win_sq = librosa_util.normalize(win_sq, norm=norm)**2
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win_sq = librosa_util.pad_center(win_sq, n_fft)
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# Fill the envelope
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for i in range(n_frames):
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sample = i * hop_length
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x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))]
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return x
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def griffin_lim(magnitudes, stft_fn, n_iters=30):
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"""
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PARAMS
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------
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magnitudes: spectrogram magnitudes
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stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods
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"""
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angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size())))
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angles = angles.astype(np.float32)
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angles = torch.autograd.Variable(torch.from_numpy(angles))
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signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
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for i in range(n_iters):
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_, angles = stft_fn.transform(signal)
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signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
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return signal
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def dynamic_range_compression(x, C=1, clip_val=1e-5):
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"""
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PARAMS
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------
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C: compression factor
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"""
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return torch.log(torch.clamp(x, min=clip_val) * C)
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def dynamic_range_decompression(x, C=1):
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"""
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PARAMS
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------
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C: compression factor used to compress
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"""
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return torch.exp(x) / C
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