Cross platform audio feature extraction and sound classification tool

Java Implementation of the Sonopy Audio Feature Extraction Library by MycroftAI

Urban Sound Annotation and Classification

Speaker recognition using Mel Frequency Cepstral Coefficients (MFCC) and Linde-Buzo-Gray (LBG) clustering algorithm

Audio input -> real-time analysis -> OSC output. Takes in real-time audio, does feature extraction using smart algorithms then sends out OSC to be used in other programs.

Tooling and datasets for neural-network powered audio feature based synthesis

Scratch for experimenting with audio feature extraction.

Convolutional-based supervised regression task for extracting high level timbral features from drums sound files, useful to condition a real time Neural Sound Synthesiser on continuous intuitive controls.

Drum Samples Clustering, Audio feature extraction and clustering audio files using data visualization and dimensionality reduction (PCA).

Various Neural Network Architectures for Supervised Tonic classification using the mridangam_stroke dataset, and supervised instrument classification on the TinySOL dataset.

A CNN model for classifying whale calls

Text-independent speaker identification system based on GMM

AudioInspect is an app that extracts audio features from uploaded audio files or audio files in a specified folder, providing insights into the characteristics of the audio.

Developed a deep learning model using Multi-Layer Perceptron to recognize and classify speech signals into 6 distinct emotions. Extracted 160 audio features, enabling the model to detect emotions with around 75% accuracy on the training set. Implemented the model on a Streamlit dashboard.

Python Script to suggest the volume at which the music audio file needs to be played for better experience and feeling.

A simple music feature extractor for Deep Learning models

GTZAN Music genre classification using Logistic regression and SVM.

Fingerprint-based music and vocals identification app that generates spectrograms, extracts features, applies perceptual hashing, and finds the most similar songs based on fingerprint matching.

TuneSpy is a Python application that allows users to load audio files, generate spectrograms, extract MFCC features, and compare the loaded audio with a preprocessed database of songs to find the most similar match.

Generation of music playlists based on audio features analysis using Essentia and the MusAV dataset