The Tabla Taal Recognition System is an advanced research project focusing on the recognition and classification of Classical Indian Music Tabla Taals. The system identifies renowned Taals like Addhatrital, Ektal, Rupak, Dadra, Deepchandi, Jhaptal, Trital, and Bhajani. By using machine learning models, including Feedforward Neural Networks (FNN) and Convolutional Neural Networks (CNN), the system effectively analyzes and categorizes complex rhythmic patterns in Indian Classical Music.
This system uses a Graphical User Interface (GUI) where users can upload audio files containing Tabla performances. Once uploaded, the system processes the audio, recognizes the Taal, and displays the classification result.
- Taal Recognition: Efficient recognition of various classical Tabla Taals.
- Machine Learning Models: Uses FNN and CNN for high-accuracy Taal classification.
- GUI for Easy Interaction: Provides a simple and user-friendly interface for uploading and analyzing audio files.
- Dataset Training: Trained on a large set of Classical Indian Music recordings for robust and accurate results.
- User Input: The user uploads an audio file through the GUI.
- Audio Processing: The audio file is processed to extract relevant features.
- Model Prediction:
- FNN and CNN models are used to predict the Taal.
- Result Display: The system displays the recognized Taal on the interface.
| Frontend and Backend |
|---|
| Python |
- 🥁 Taal Classification: Categorizes various Classical Indian Music Tabla Taals.
- 📊 FNN & CNN: Utilizes state-of-the-art Feedforward and Convolutional Neural Networks for analysis.
- 🎛️ User-Friendly Interface: Simple interface for uploading audio files and viewing results.
- 📈 Accurate Results: Highly accurate recognition through a trained model on Indian Classical Music data.