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Tabla Strokes Recognition

Tabla Strokes Recognition

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Tabla Strokes Recognition

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  1. Tabla Strokes Recognition Mihir Sarkar

  2. Tabla?

  3. Context • Can you distinguish different bols? • Can a machine automatically classify tabla strokes? • Is there a systematic way to identify the best method to recognize tabla strokes?

  4. Vision

  5. Experimental Setup • 1 tabla set • 3 tabla players • 10 bols • 413 recordings (kept 300) • Microphone input (studio recording) • Discrete strokes

  6. Raw data

  7. Spectrogram

  8. Feature Extraction • Time domain: ZCR • Frequency domain: PSD • Cepstral domain: MFCC

  9. Dataset Selection • Orthogonal dimensions: • Instances • Bols • Players • Training / leave-one-out validation • Testing

  10. Baseline • Random: 10% • Human: 87% • Initial k-NN: 18%(Welch’s PSD, NFFT = 16, k = 1)

  11. k-NN

  12. k-NN

  13. k-NN

  14. k-NN

  15. k-NN

  16. k-NN

  17. Confusion Matrix

  18. Neural Networks

  19. Contributions • Implemented pattern classification algorithms (Matlab) • Analyzed recognition rates with varying parameters • Explored a systematic way to perform classification

  20. Future Directions • Vibration sensors • More recordings • Timing (multiple frames, HMM) • Real-time • Continuous strokes • Integrate context (rhythmic patterns)