A Python library for audio and music analysis, feature extraction

[CMLab]

Open source project:
https://github.com/libAudioFlux/audioFlux

AudioFlux is a library for audio and music analysis and feature extraction, which supports dozens of time-frequency analysis and transformation methods, as well as hundreds of corresponding time-domain and frequency-domain feature combinations, which can be provided to the deep learning network for training and can be used to study the classification, separation, music information retrieval (MIR), ASR and other tasks in the audio field.




Code Demo
An example of Mel and MFCC features.

pip install audioflux

import numpy as np import audioflux as af import matplotlib.pyplot as plt from audioflux.display import fill_spec # Get a 220Hz's audio file path sample_path = af.utils.sample_path('220') # Read audio data and sample rate audio_arr, sr = af.read(sample_path) # Extract mel spectrogram spec_arr, mel_fre_band_arr = af.mel_spectrogram(audio_arr, num=128, radix2_exp=12, samplate=sr) spec_arr = np.abs(spec_arr) # Extract mfcc mfcc_arr, _ = af.mfcc(audio_arr, cc_num=13, mel_num=128, radix2_exp=12, samplate=sr) # Display audio_len = audio_arr.shape[0] # calculate x/y-coords x_coords = np.linspace(0, audio_len / sr, spec_arr.shape[1] + 1) y_coords = np.insert(mel_fre_band_arr, 0, 0) fig, ax = plt.subplots() img = fill_spec(spec_arr, axes=ax, x_coords=x_coords, y_coords=y_coords, x_axis='time', y_axis='log', title='Mel Spectrogram') fig.colorbar(img, ax=ax) fig, ax = plt.subplots() img = fill_spec(mfcc_arr, axes=ax, x_coords=x_coords, x_axis='time', title='MFCC') fig.colorbar(img, ax=ax) plt.show()