In this project, I implemented Convolutional Neural Networks on images of melspectrogram of sound files.

The goal of this project is to obtain a classifier that can automatically classify environmental sounds according to their category. This can be implemented on both transport vehicles and wearable devices to improve road safety.

Audio Classification on UrbanSound8k dataset

Urban Sound Classification

Classification of urban sounds such as air conditioner, jackhammer, drilling, siren, street music, engine idling and children playing by using Mel-frequency Cepstral Coefficients (MFCCs) as audio feature and CNN algorithm.

A deep learning classifier for urban sounds using the EfficientNet network

Credit card fraud detection, Breast cancer prediction, Wine quality prediction, Bank note authentication, prediction of attrition of employees, Stock prediction, etc

A CNN model trained using spectrograms of urban sounds

consist of python scripts, having various models for urban sound classification on UrbanSound8K dataset based on http://aqibsaeed.github.io/2016-09-03-urban-sound-classification-part-1/

[SCH졸업논문] 위급한 상황의 소리를 딥러닝으로 인식하여 알려줍니다

Urban Sounds Classification - Koç Holding Deep Learning Bootcamp by GlobalAIHub

Spectrogram for UrbanSound8K audio dataset

My Urban Sound Challenge classification

Web app for urban sound classification

Waveplot for UrbanSound8K audio dataset

Urban Sound Challenge project

Classifying Sounds

Sound classification on Urban Sound Dataset

Set of 1D CNN models to classify sound clips from the Urban Sound Classification dataset using Keras and Librosa

Sound Classification using Neural Networks