metadata

title: EcoSoundNet
emoji: 🔥
colorFrom: pink
colorTo: blue
sdk: streamlit
sdk_version: 1.41.1
app_file: app.py
pinned: false

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

EcoSoundNet 🔥

Welcome to EcoSoundNet, a sound classification application built using Streamlit. This project leverages audio data from the UrbanSound8K dataset and implements machine learning models for sound classification. Check out the details below!

Overview

EcoSoundNet uses state-of-the-art techniques for audio classification, aiming to detect various environmental sounds. The model is trained on the UrbanSound8K dataset, which contains 8,732 labeled sound excerpts from urban environments. The project includes preprocessing steps, model training, and validation with accuracy comparisons.

Dataset

The dataset used for training the model is the UrbanSound8K dataset. This dataset consists of sound clips from 10 different urban sound classes, making it suitable for environmental sound classification tasks.

Notebooks for Preprocessing, Training, and Accuracy Comparisons

For detailed insights into the preprocessing, training, and validation steps, please refer to the following notebook:

Training and Validation Notebook

This notebook covers:

Data preprocessing steps
Model training
Accuracy comparisons of different models

Streamlit App

The main application is built using Streamlit, a powerful framework for creating data science applications. You can run the app using the file app.py.

Configuration

SDK: Streamlit
SDK Version: 1.41.1
App File: app.py
Pinned: No