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

librosa.display.waveplot() no longer supported by librosa, use librosa.display.waveshow()

https://github.com/musikalkemist/AudioSignalProcessingForML/blob/master/8-%20Implementing%20the%20amplitude%20envelope/Implementing%20the%20amplitude%20envelope.ipynb

librosa no longer supporting first parameter as y
use y= to address the right parameter
librosa.feature.rms(y=[your loaded file])

Hi! I am excited in this calculation. Currently, the calculation is to calculate the whole piece, but I am wondering is there any way to get the array of envelopes in every second? I mean in the first second, the amplitude_envelope is [[0.00000000e+00, 2.32199546e-02, 4.64399093e-02, ..., 4.00544218e+01, 4.00776417e+01, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00], and second is..., and so on.

Thanks!

Hi,
I am working on the feature extraction (Mel-spectrogram) for training the ML model for Sound event detection.
I have a doubt regarding the steps required for the feature extraction process. Is it mandatory/required to convert the Amplitude to dB scale (librosa.power_to_db) or I can proceed without it? As after calculating Mel-power Spectrogram I am getting -ve values also which is creating a problem in Log-compression as -ve values give NaN.

Note: I am normalizing a file with its own mean and std.