pyAudioProcessing 1.1.5

Audio processing-feature extraction and building machine learning models from audio data.

pyAudioProcessing

A Python based library for processing audio data into features and building Machine Learning models.
This was written using Python 3.7.6, and should work with python 3.6+.

Getting Started

Clone the project and get it setup

git clone [e-mail]:jsingh811/pyAudioProcessing.git
pip install -e .

Get the requirements by running

pip install -r requirements/requirements.txt

Training and Classifying Audio files

Choices

Feature options :
You can choose between mfcc, gfcc or gfcc,mfcc features to extract from your audio files.
Classifier options :
You can choose between svm, svm_rbf, randomforest, logisticregression, knn, gradientboosting and extratrees.
Hyperparameter tuning is included in the code for each using grid search.

Examples

Command line example of using gfcc feature and svm classifier.

Training:

python pyAudioProcessing/run_classification.py -f "data_samples/training" -clf "svm" -clfname "svm_clf" -t "train" -feats "gfcc"

Classifying:

python pyAudioProcessing/run_classification.py -f "data_samples/testing" -clf "svm" -clfname "svm_clf" -t "classify" -feats "gfcc"

Classification results get saved in classifier_results.json.

Code example of using gfcc feature and svm classifier.

from pyAudioProcessing.run_classification import train_and_classify
# Training
train_and_classify("data_samples/training", "train", ["gfcc"], "svm", "svm_clf")
# Classify data
train_and_classify("data_samples/testing", "classify", ["gfcc"], "svm", "svm_clf")

Extracting features from audios

This feature lets the user extract data features calculated on audio files.

Choices

Feature options :
You can choose between mfcc, gfcc or gfcc,mfcc features to extract from your audio files.
To use your own audio files for feature extraction, refer to the format of directory data_samples/testing.

Examples

Command line example of for gfcc and mfcc feature extractions.

python pyAudioProcessing/extract_features.py -f "data_samples/testing"  -feats "gfcc,mfcc"

Features extracted get saved in audio_features.json.

Code example of performing gfcc and mfcc feature extraction.

from pyAudioProcessing.extract_features import get_features
# Feature extraction
features = get_features("data_samples/testing", ["gfcc", "mfcc"])

Author

Jyotika Singh
Data Scientist
https://twitter.com/jyotikasingh_/ https://www.linkedin.com/in/jyotikasingh/