| import sys |
| import numpy as np |
| from scipy import signal |
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| |
| def preemphasis(data, coeff=0.97): |
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| return signal.lfilter([1,-coeff], [1], data).astype(np.float32) |
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| def inv_preemphasis(data, coeff=0.97): |
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| return signal.lfilter([1], [1,-coeff], data).astype(np.float32) |
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|
| def lowpass(data, stop_freq, sample_freq, N=4): |
| wn = 2.0 * stop_freq / sample_freq |
| b, a = signal.butter(N, wn, btype="low") |
| data = signal.filtfilt(b,a, data) |
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| return data |
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|
| def tfconvert(x, window_len, hop_len, mult, window='hann') : |
| noverlap = window_len - hop_len |
| _, _, y = signal.stft(x, window=window, nperseg=window_len, noverlap=noverlap) |
|
|
| y_re = np.real(y) * (window_len//2 + 1) |
| y_im = np.imag(y) * (window_len//2 + 1) |
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| y_mag = np.log(np.sqrt(y_re ** 2 + y_im ** 2)+1.0).astype(np.float32) |
| y_phase = np.arctan2(y_im, y_re).astype(np.float32) |
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|
| y_mag = zero_pad(y_mag, mult) |
| y_phase = zero_pad(y_phase, mult) |
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| return y_mag, y_phase |
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|
| def zero_pad(x, mult) : |
| mod = x.shape[2] % mult |
| if mod > 0 : |
| pad = mult - mod |
| x = np.concatenate(( x, np.zeros((x.shape[0], x.shape[1], pad), dtype=np.float32) ), axis=2) |
| return x |
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|
| def calc_time(sample_len ,sr) : |
| quot = sample_len // sr |
| rem = (sample_len % sr) / sr |
| min = quot // 60 |
| sec = quot % 60 + rem |
| print('Time length : {}min {:.02f}sec'.format(min,sec)) |
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|
