Logmel Spectrogram- feature extraction

[shivashankarivr]

Hi, am doing speech recognition for micro controller. Am new to this and trying to modify the code which is written for Acoustic Scene Classification where they have used 30sec wav audio dataset.

Now, I need to use 1sec dataset for speech recognition but am not getting proper value after feature extraction.

Below are the codes which am using for log mel spectrogram. Can help me pls?

"""LogMel Feature Extraction example."""

import numpy as np import sys import librosa import librosa.display import scipy.fftpack as fft

SR = 16000 N_FFT = 1024 N_MELS = 30

def create_col(y): assert y.shape == (1024,)

# Create time-series window
fft_window = librosa.filters.get_window('hann', N_FFT, fftbins=True)
assert fft_window.shape == (1024,), fft_window.shape

# Hann window
y_windowed = fft_window * y
assert y_windowed.shape == (1024,), y_windowed.shape

# FFT
fft_out = fft.fft(y_windowed, axis=0)[:513]
assert fft_out.shape == (513,), fft_out.shape

# Power spectrum
S_pwr = np.abs(fft_out)**2

assert S_pwr.shape == (513,)

# Generation of Mel Filter Banks
mel_basis = librosa.filters.mel(SR, n_fft=N_FFT, n_mels=N_MELS, htk=False)
assert mel_basis.shape == (30, 513)

# Apply Mel Filter Banks
S_mel = np.dot(mel_basis, S_pwr)
S_mel.astype(np.float32)
assert S_mel.shape == (30,)

return S_mel

def feature_extraction(y): assert y.shape == (32, 1024)

S_mel = np.empty((30, 32), dtype=np.float32, order='C')
for col_index in range(0, 32):
    S_mel[:, col_index] = create_col(y[col_index])

# Scale according to reference power
S_mel = S_mel / S_mel.max()
# Convert to dB
S_log_mel = librosa.power_to_db(S_mel, top_db=80.0)
assert S_log_mel.shape == (30, 32)

return S_log_mel

[shivashankarivr]

What changes should I need to make for 1sec long audio?