sound-classification-spectrograms

Urban sound source tagging from an aggregation of four second noisy audio clips via 1D and 2D CNN (Xception)

Fully automated machine learning pipeline for bird sound recognition

Sound classification on Urban Sound Dataset

This repository contains code for classification of sound using spectrograms. We train a CNN to classify the sounds after converting to spectrogram.

[ICASSP 2023] PyTorch code for "Play It Back: Iterative Attention for Audio Recognition"

This project explores the use of deep learning models, particularly CNNs, to classify drum sounds accurately.

Drone malfunction finder is an end-to-end system for fault-detection-from-uav-sounds, leveraging ML-based audio analysis to identify and classify drone faults.

This is a machine learning/neural network model that can classify the type of sound (10 classes) using the Mel Scale and Spectrogram.

Replication of the Paper Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification by Salomon & Bello

Implementation of sound classifying program.

Mini-Contests of Machine Learning course

Code from kaggle competition

🐦 Deep Learning based Bird Sound Classification covering 50 species. Powered by PyTorch, Librosa, ResNet-18 Transfer Learning and Smart Audio Slicing. Trained with Xero-Canto datasets. Achieved ~96% accuracy.