Music Genre Classification using Neural Networks

This project implements a Convolutional Neural Network (CNN) to classify music genres from audio files. The model uses Mel-Frequency Cepstral Coefficients (MFCCs) as features to train a deep learning model that can accurately predict the genre of a given audio clip.

Dataset

The project uses the GTZAN Genre Collection Dataset, which contains:

• 1000 audio files (30 seconds each)
• 10 genres: Blues, Classical, Country, Disco, Hip-hop, Jazz, Metal, Pop, Reggae, Rock

Features

• Audio preprocessing using Librosa
• MFCC feature extraction
• CNN-based deep learning model
• Model evaluation and visualization
• Early stopping to prevent overfitting

Requirements

Install the required packages using:

pip install -r requirements.txt

Project Structure

• music_genre_classification.py: Main script containing the implementation
• requirements.txt: List of required Python packages
• Data/: Directory containing the GTZAN dataset

Usage

1. Make sure you have the GTZAN dataset in the Data/genres_original directory
2. Run the main script:

python music_genre_classification.py

The script will:

• Load and preprocess the audio files
• Extract MFCC features
• Train the CNN model
• Evaluate the model's performance
• Save the trained model as music_genre_classifier.h5

Model Architecture

The CNN model consists of:

• 3 Convolutional layers with MaxPooling and Dropout
• Flatten layer
• Dense layers with Dropout
• Output layer with softmax activation

Performance

The model achieves an accuracy of approximately 70-90% on the test set, depending on the training parameters and data preprocessing.

Visualization

The script generates plots showing:

• Training and validation accuracy over epochs
• Training and validation loss over epochs