esc-50

[ICLR 2024] SemiReward: A General Reward Model for Semi-supervised Learning

Building a Sound Classification iOS Application using AI

This repository contains an end-to-end pipeline to train a convolutional neural network (CNN) for environmental sound classification on ESC-50. Serve the model for inference using Modal + a FastAPI endpoint. - Visualize model outputs (top predictions, input mel-spectrogram, waveform, and CNN feature maps).

Classification of Environment Sound using CNN and ImageDataGenerator

audio classification by pytorch.

Classification of Environmental Sound using Deep Learning.

Audio Diffusion

A deep learning classifier for urban sounds using the EfficientNet network

REST API based on PyTorch (ResNet18) for classifying 50 categories of natural and household sounds (rain, chainsaw, glass breaking, etc.) from audio files. Mel spectrograms + FastAPI. Val accuracy 86%. Trained in Google Colab on ESC-50.

This project builds a system to split spoken sentences into words (speaker-independent) and calculates the speaker's average pitch, comparing different methods to identify word boundaries.

Reconnaissance de sons du quotidien (ESC-50) avec des descripteurs audio + XGBoost, sans deep learning

Residual CNN for Environmental Sound Classification — 84.5% accuracy on ESC-50, outperforming SVM baseline by 22.5%

Exploration of various machine learning techniques to classify audio data using the ESC-50 dataset.

End to End ML Classification with Streamlit Frontend

The aim of this project was to design and implement a Flask web application for classifying environmental sounds which uses convolutional neural network architecture.

Environmental sound classification using MFCC features, Random Forest, and a Streamlit web app.