Python library for audio and music analysis

AudioMuse-AI is an Open Source Dockerized environment that brings automatic playlist generation to Jellyfin, Navidrome, LMS, Lyrion and Emby. Using powerful tools like Librosa and ONNX, it performs sonic analysis on your audio files locally, allowing you to curate the perfect playlist for any mood or occasion without relying on external APIs.

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

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

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

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

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

Easier audio-based machine learning with TensorFlow.

NineSong aims to provide Cloud native and AI extended solutions for data sharing in various ToC businesses

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

Human Emotion Understanding using multimodal dataset.

(monophonic) audio to midi converter using Python and librosa

Python framework for Speech and Music Detection using Keras.

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。

AudioMuse-AI Jellyfin Plugin

Music Synthesis with Python talk, originally given at PyGotham 2017.

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

Music genre classification model using CRNN

Image Processing, Speech Processing, Encoder Decoder, Research Paper implementation

Music genre classification from audio spectrograms using deep learning

Advanced ML Project : An Orca Call classifier using mel-spectrograms as audio representations to detect Killer whales

We'll look into audio categorization using deep learning principles like Artificial Neural Networks (ANN), 1D Convolutional Neural Networks (CNN1D), and CNN2D in this repository. We undertake some basic data preprocessing and feature extraction on audio sources before developing models. As a result, the accuracy, training time, and prediction time of each model are compared. This is explained by model deployment, which allows users to load the desired sound output for each model that is successfully deployed, as will be addressed in more depth later.

Sound Classification using Neural Networks

Predicting various emotion in human speech signal by detecting different speech components affected by human emotion.

MIMII Sound Anomaly Detection with AutoEncoders

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