An approach to extract most of the features from an audio file

Run the following command in command shell

foo@baar python3 -m virtualenv audio

Then activate the given virtual envirionement by running the following command in command shell :

foo@bar source env/bin/activate

Run the following command in command shell

foo@bar pip install -r requirement.txt

This will install all the necessary packages that is required to extract the features from an audio.

This file conatains the code that can find the pitch value with the help of a library aubio in python. Then with the help of pitch we can find other features like voice breaks, max/min/mean pitch value and many more see wiki for details. In order to see the result run the following command

foo@bar python pitch_feature.py /audio/human.wav

This file contain the code for finding the energy value and its representation in terms of band. It also uses the same library aubio as above. In order to see the result run the following command

foo@bar python energy.py /audio/human.wav

This will display the energy value at each regular interval as well as the energy band as a graph.

This file is used to represent the audio in different format like spectrogram, spectrogram roll off, spectrogram centroid, mfcc etc.It uses the library librosa in python See the result by running this code

foo@bar python audio_graph.py /audio/human.wav

The importnace of spectrogram is that it can easily be used as an input feature to any neural networl which can be used to extract some important features.

This file contain the code that can be used to extract some of the measure and important features from the audio file. In order to see the result from this file run the following command in command shell

foo@bar python extra_feature_extract.py human.wav

This file contains the functoin for creating different form of waveform for the file energy.py and pitch_feature.py.