The Pytorch implementation of sound classification supports EcapaTdnn, PANNS, TDNN, Res2Net, ResNetSE and other models, as well as a variety of preprocessing methods.

A library built for easier audio self-supervised training, downstream tasks evaluation

基于Tensorflow实现声音分类，博客地址：

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

Sound Classification using Librosa, ffmpeg, CNN, Keras, XGBOOST, Random Forest.

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

UrbanSound8K dataset classification using MLP and CNN

Urban Sound Annotation and Classification

In this repository you will find an end to end hands-on tutorial of an example of machine learning in production. The objective will be to create and deploy in the cloud a machine learning application able to recognize and classify different audio sounds.

Car honk detector using Edge Impulse

An Audio Classification Project Using ML & DL on Urbansound8K Dataset (Kaggle): Sound Classification using Librosa, MFCC, CNN, Keras, XGBOOST, Random Forest.

Urban Sound classification using Machine learning

A deep learning classifier for urban sounds using the EfficientNet network

Application of a convolutional neural network (CNN) to accurately classify urban sounds in a bid to increase pedestrian safety using the UrbanSound8k dataset.

This project classifies urban noise using machine learning models such as DNN, CNN, LSTM, and Random Forest. Utilizing the UrbanSound8K dataset, it aims to accurately identify different urban sounds, aiding in noise monitoring and management. Key features include robust model comparison and real-time deployment potential.

Audio classification, denoising & generation of new samples on UrbanSound8K dataset (UniPD)

Acustic events classifictaion using CNN on UrbanSound8K dataset

Urban Sounds Classification - Koç Holding Deep Learning Bootcamp by GlobalAIHub

Audio Classification with CNN

Explore advanced audio classification with SimCLR-UrbanSound8K. This repository applies SimCLR for urban sound categorization using the UrbanSound8K dataset, demonstrating state-of-the-art techniques in deep learning and audio analysis