A library for audio and music analysis, feature extraction.

Audio feature extraction for JavaScript.

Extract video features from raw videos using multiple GPUs. We support RAFT flow frames as well as S3D, I3D, R(2+1)D, VGGish, CLIP, and TIMM models.

Fast and simple music and audio analysis using RNN in Python 🕵️‍♀️ 🥁

Python scripts accompanying the book "An Introduction to Audio Content Analysis" (www.AudioContentAnalysis.org)

Package for automatic beat-mixing of music files in Python 🐻🎚

Evolving Artificial Neural Networks for Cross-Adaptive Audio Effects

Matlab scripts accompanying the book "An Introduction to Audio Content Analysis" (www.AudioContentAnalysis.org)

C++ code accompanying the book "An Introduction to Audio Content Analysis" (www.AudioContentAnalysis.org)

Audio Commons Audio Extractor

Data analysis on my monthly playlists

Emotional Video to Audio Transformation with ANFIS-DeepRNN (Vanilla RNN and LSTM-DeepRNN) [MPE 2020]

Extract frequency, power, width and dissonance of formants from wav files

Show trends about your music listening habits over time. Create playlists from a given seed and specified audio features.

This may become an audio feature extraction library for Rust.

Draw insights on Spotify users’ musical tastes and generate playlists tailored to either a single user or a group of users by analyzing their Top 100 tracks.

Cross platform audio feature extraction and sound classification tool

Estratto is a powerful and user-friendly Rust library designed for extracting rich audio features from digital audio signals.

The aim of this project is to map and analyse the sentiments of different countries based on their music tastes over time. The data is presented in a web app coded in Python using the Plotly Dash package.

Features from audio: Spectrogram, (Wavelet Transform) Scalogram, (Q Transform) Spectrogram