Vikramansen/BeatBrainiac

BeatBrainiac is an advanced Music Genre Classification tool utilizing LSTM (Long Short-Term Memory) networks, alongside Time and Frequency Domain Features. This project aims to accurately classify music into various genres, providing a unique blend of time-series analysis and machine learning techniques.

BeatBrainiac

Implemented a Research paper on Music Genre Classification by emplying LSTM.

BeatBrainiac is an advanced Music Genre Classification tool utilizing LSTM (Long Short-Term Memory) networks, alongside Time and Frequency Domain Features. This project aims to accurately classify music into various genres, providing a unique blend of time-series analysis and machine learning techniques.

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Installation and Usage

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)

Installation

1. Clone the Repository

   git clone [https://github.com/Vikramansen/BeatBrainiac.git]
   cd BeatBrainiac
   

2. Install Dependencies

   pip install -r requirements.txt
   

Usage

1. Run Preprocessing

   • Execute preprocessing.ipynb to prepare your dataset.

2. Train and Evaluate Models

   • Use LSTM.ipynb for LSTM model training and evaluation.
   • Optionally, explore SVM_KNN.ipynb for SVM and KNN models.

3. Perform Classification

   • Apply the trained models in classification.ipynb.

4. Utilize Utilities

   • Employ utils.py for additional functions.

Running Notebooks

• Launch Jupyter Notebooks:

  jupyter notebook
  

• Open and run each notebook via the Jupyter interface.

Troubleshooting

• For issues, ensure pip is up-to-date and retry dependencies installation.

Support

• For assistance or bug reporting, contact me or use the issue tracker on GitHub.