Python library for audio and music analysis

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.

Lightweight and Interpretable ML Model for Speech Emotion Recognition and Ambiguity Resolution (trained on IEMOCAP dataset)

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.

LibrosaCpp is a c++ implemention of librosa to compute short-time fourier transform coefficients,mel spectrogram or mfcc

Easier audio-based machine learning with TensorFlow.

Speech Emotion Recognition (SER) in real-time, using Deep Neural Networks (DNN) of Long Short Memory Term (LSTM).

(monophonic) audio to midi converter using Python and librosa

Python framework for Speech and Music Detection using Keras.

AudioMuse AI: Leverages Librosa for sonic analysis and AI-powered clustering to create smart, tempo and mood-based playlists within Jellyfin and Navidrome API

Human Emotion Understanding using multimodal dataset.

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

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

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 Synthesis with Python talk, originally given at PyGotham 2017.

Music genre classification model using CRNN

Environmental sound classification with Convolutional neural networks and the UrbanSound8K dataset.