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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.
An Audio Classification Project Using ML & DL on Urbansound8K Dataset (Kaggle): Sound Classification using Librosa, MFCC, CNN, Keras, XGBOOST, Random Forest.
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
Application of a convolutional neural network (CNN) to accurately classify urban sounds in a bid to increase pedestrian safety using the UrbanSound8k dataset.
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.
The Pytorch implementation of sound classification supports EcapaTdnn, PANNS, TDNN, Res2Net, ResNetSE and other models, as well as a variety of preprocessing methods.