UrbanSound8K Audio Classifier 🎧

This project is a Streamlit web app that classifies environmental sounds from audio files using a deep learning model trained on the UrbanSound8K dataset.

🔍 Overview

The app lets users upload a .wav or mp3 file, extracts Mel-Frequency Cepstral Coefficients (MFCCs) using librosa, and predicts the sound category using a pre-trained TensorFlow model.

🚀 Demo

Try the app on Streamlit Cloud:

👉 Launch App

Upload any .wav audio file to see it in action!

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🎯 Predicted Classes

The model can recognize the following 10 environmental sound classes:

Class
air_conditioner
children_playing
jackhammer
dog_bark
drilling
engine_idling
street_music
siren
car_horn
gun_shot

🧠 Model Details

• Framework: TensorFlow / Keras
• Feature extraction: MFCC (40 coefficients)
• Architecture: Feedforward neural network with dropout layers
• Optimizer: Adam
• Loss function: Categorical Crossentropy

📁 Project Structure

urbansound8k-classifier/
│
├── app.py # Streamlit frontend
├── model.keras # Trained classification model
├── labelencoder.pkl # Fitted label encoder
├── requirements.txt # Python dependencies
├── runtime.txt # Python version for Streamlit (optional)
└── README.md # This file

⚙️ Installation & Local Run

📦 Clone the repository

git clone https://github.com/arjunverma2004/ANN-CustomerChurn-classification.git
cd ANN-CustomerChurn-classification

🐍 Create a virtual environment (recommended)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

🔧 Install dependencies

pip install -r requirements.txt

▶️ Run the Streamlit App Locally

streamlit run app.py

📦 Deployment

This app is deployable on Streamlit Cloud. Ensure the following files are included in the root of your GitHub repo:

• app.py

• model.keras

• labelencoder.pkl

• requirements.txt

✍️ Author

Arjun Verma

• GitHub: @arjunverma2004
• LinkedIn: Arjun Verma