Cross platform audio feature extraction and sound classification tool

Java Implementation of the Sonopy Audio Feature Extraction Library by MycroftAI

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.

Urban Sound Annotation and Classification

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).

Trained a CNN model to classify whale calls into an A-call or not

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

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.

GTZAN Music genre classification using Logistic regression and SVM.

A simple music feature extractor for Deep Learning models

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

Twenor is a conceptual platform designed to assist artists with advanced tools for audio classification, cover art creation, and music management. Envisioned to integrate neural network-based classification, customizable cover art design, and Recordbox XML support, Twenor aims to streamline and enhance the music production workflow—all for free.

A server to host JAMS audio feature extraction data