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ift6266h14/sparse_coding/sparse_coding_gammatone.py
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import numpy as np | |
from sklearn.decomposition import SparseCoder | |
def gammatone_function(resolution, fc, center, fs=16000, l=4, | |
b=1.019): | |
t = np.linspace(0, resolution-(center+1), resolution-center)/fs | |
g = np.zeros((resolution,)) | |
g[center:] = t**(l-1) * np.exp(-2*np.pi*b*erb(fc)*t)*np.cos(2*np.pi*fc*t) | |
return g | |
def gammatone_matrix(b, fc, resolution, step, fs=16000, l=4, threshold=1e-2): | |
"""Dictionary of gammatone functions""" | |
centers = np.arange(0, resolution - step, step) | |
D = [] | |
for i, center in enumerate(centers): | |
t = np.linspace(0, resolution-(center+1), resolution-center)/fs | |
env = t**(l-1) * np.exp(-2*np.pi*b*erb(fc)*t) | |
if env[-1]/max(env) < threshold: | |
D.append(gammatone_function(resolution, fc, center, b=b, l=l)) | |
D = np.asarray(D) | |
D /= np.sqrt(np.sum(D ** 2, axis=1))[:, np.newaxis] | |
return D | |
def erb(f): | |
return 24.7+0.108*f | |
def erb_space(low_freq, high_freq, num_channels, EarQ = 9.26449, minBW = 24.7, order = 1): | |
return -(EarQ*minBW) + np.exp(np.arange(1,num_channels+1)*(-np.log(high_freq + EarQ*minBW) + np.log(low_freq + EarQ*minBW))/num_channels) * (high_freq + EarQ*minBW) | |
if __name__ == '__main__': | |
from scipy.io import wavfile | |
from scikits.talkbox import segment_axis | |
resolution = 160 | |
step = 8 | |
b = 1.019 | |
n_channels = 64 | |
overlap = 80 | |
# Compute a multiscale dictionary | |
D_multi = np.r_[tuple(gammatone_matrix(b, fc, resolution, step) for | |
fc in erb_space(150, 8000, n_channels))] | |
# Load test signal | |
fs, y = wavfile.read('/home/jfsantos/data/TIMIT/TRAIN/DR1/FCJF0/SA1.WAV') | |
y = y / 2.0**15 | |
Y = segment_axis(y, resolution, overlap=overlap, end='pad') | |
Y = np.hanning(resolution) * Y | |
# segments should be windowed and overlap | |
coder = SparseCoder(dictionary=D_multi, transform_n_nonzero_coefs=None, transform_alpha=1., transform_algorithm='omp') | |
X = coder.transform(Y) | |
density = len(np.flatnonzero(X)) | |
out= np.zeros((np.ceil(len(y)/resolution)+1)*resolution) | |
for k in range(0, len(X)): | |
idx = range(k*(resolution-overlap),k*(resolution-overlap) + resolution) | |
out[idx] += np.dot(X[k], D_multi) | |
squared_error = np.sum((y - out[0:len(y)]) ** 2) | |
wavfile.write('reconst_%d_%d.wav'%(resolution,overlap), fs, np.asarray(out, dtype=np.float32)) |