Python library for audio and music analysis

AudioMuse-AI is a self-hosted, Dockerized music playlist generator using sonic analysis to discover related tracks for Jellyfin, Navidrome, LMS, Lyrion, and Emby: no metadata or external APIs needed.

Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

Understanding emotions from audio files using neural networks and multiple datasets.

A Machine Learning Approach of Emotional Model

A command-line music video generator based on rhythm

An open-source Python library for audio time-scale modification.

Easier audio-based machine learning with TensorFlow.

Python framework for Speech and Music Detection using Keras.

(monophonic) audio to midi converter using Python and librosa

基于PaddlePaddle实现的音频分类，支持EcapaTdnn、PANNS、TDNN、Res2Net、ResNetSE等各种模型，还有多种预处理方法

In this project is presented a simple method to train an MLP neural network for audio signals. The trained model can be exported on a Raspberry Pi (2 or superior suggested) to classify audio signal registered with USB microphone

一个简单的小网页，录入人声哼唱，转化成钢琴音及钢琴谱输出。灵感稍纵即逝，本项目的目标是能够记录下一段小调，以音频形式输入，读取识别其曲调，并制成谱子，最终以钢琴弹奏的形式输出，依此将一些日常生活中的小灵感保存起来，以便日后回忆甚至再创作。

Music genre classification model using CRNN

Music genre classification from audio spectrograms using deep learning

Training pipeline for an osu!mania 7k next-event model, designed as the upstream predictor for audio-driven 7k map generation systems.

Binary classification problem that aims to classify human voices from audio recordings. Implemented using PyTorch and Librosa.

🔇 A production-grade deep learning system for real-time drone/UAV detection through acoustic signature analysis. Converts raw audio to Mel-Spectrograms and classifies using a custom CNN. Features auto-dataset ingestion, defense-optimized metrics (high recall), early stopping, model checkpointing, and a ready-to-use inference API.

Artificial intelligence bot for live voice improvisation

100行代码实现简单音乐卡点