https://github.com/hridxyz/music-genre-classification

A deep learning model to classify music audio into 10 genres using Convolutional Neural Networks (CNNs). Achieved over 97% training accuracy and 90% validation accuracy.
https://github.com/hridxyz/music-genre-classification

audio-classification cnn deep-learning keras machine-learning music-genre-classification tensorflow

Last synced: 5 days ago
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A deep learning model to classify music audio into 10 genres using Convolutional Neural Networks (CNNs). Achieved over 97% training accuracy and 90% validation accuracy.

• Host: GitHub
• URL: https://github.com/hridxyz/music-genre-classification
• Owner: Hridxyz
• Created: 2024-08-15T09:39:58.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-15T10:43:04.000Z (3 months ago)
• Last Synced: 2024-10-10T08:22:59.517Z (29 days ago)
• Topics: audio-classification, cnn, deep-learning, keras, machine-learning, music-genre-classification, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 20 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Music Genre Classification Using Deep Learning

## Project Overview

This project involves developing a deep learning model to classify music audio into 10 distinct genres. The model leverages Convolutional Neural Networks (CNNs) to process audio features extracted from music tracks, achieving high accuracy on both training and validation datasets.

## Features

- **Music Genre Classification**: Classifies audio files into 10 genres such as Jazz, Blues, Classical, etc.

- **Deep Learning Model**: Built with TensorFlow and Keras, using CNNs optimized for audio data.

- **Data Processing**: Utilizes Librosa for audio feature extraction, including Mel-frequency cepstral coefficients (MFCCs).

- **High Accuracy**: Achieved over 97% accuracy on training data and approximately 90% on validation data.

## Project Structure

- **Train_Music_Genre_Classifier.ipynb**: Jupyter notebook containing the training process, including data preprocessing, model architecture, and training loop.

- **Test_Music_Genre.ipynb**: Jupyter notebook for testing the trained model on unseen data and evaluating its performance.

- **training_hist.json**: JSON file containing the training history, including loss and accuracy metrics for both training and validation datasets.

- **Audio Files**: Example audio files used for training and testing, e.g., `blues.00000.wav`, `jazz.00054.wav`.

## Installation

To run this project locally, follow these steps:

1. **Clone the repository**:

    ```bash

    git clone https://github.com/hridxyz/Music-Genre-Classification.git

    cd Music-Genre-Classification

    ```

2. **Install required dependencies**:

    ```bash

    pip install -r requirements.txt

    ```

3. **Run the training notebook**:

    Open `Train_Music_Genre_Classifier.ipynb` in Jupyter Notebook and execute the cells to train the model.

4. **Test the model**:

    Open `Test_Music_Genre.ipynb` in Jupyter Notebook to test the trained model and see the results.

## Dependencies

- Python 3.7+

- TensorFlow

- Keras

- Librosa

- Pandas

- NumPy

- Matplotlib

- Jupyter Notebook

## Usage

To classify a new music track, add your audio file to the appropriate location and run the test notebook. The model will output the predicted genre.

## Results

The model was trained on a large dataset of labeled music tracks and tested with unseen data. The final model achieved:

- **Training Accuracy**: 97.7%

- **Validation Accuracy**: 90.3%

## Contributing

Contributions are welcome! If you'd like to improve the project, feel free to fork the repository and submit a pull request.