Tabla Strokes Recognition

Tabla Strokes Recognition

Mihir Sarkar

Tabla?

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?

Vision

Experimental Setup

• 1 tabla set

• 3 tabla players

• 10 bols

• 413 recordings (kept 300)

• Microphone input (studio recording)

• Discrete strokes

Raw data

Spectrogram

Feature Extraction

• Time domain: ZCR

• Frequency domain: PSD

• Cepstral domain: MFCC

Dataset Selection

• Orthogonal dimensions:– Instances– Bols– Players

• Training / leave-one-out validation

• Testing

Baseline

• Random: 10%

• Human: 87%• Initial k-NN: 18%

(Welch’s PSD, NFFT = 16, k = 1)

k-NN

k-NN

k-NN

k-NN

k-NN

k-NN

Confusion Matrix

Na Tin Ga Ka Dha Dhin Te Re Tat Thun

Na 5 0 0 0 0 0 0 0 0 1

Tin 0 3 0 0 1 0 0 0 0 2

Ga 0 0 4 0 0 2 0 0 0 0

Ka 0 1 0 3 0 1 0 1 0 0

Dha 0 0 2 0 2 1 1 0 0 0

Dhin 0 0 1 0 1 4 0 0 0 0

Te 0 1 0 0 0 0 1 0 4 0

Re 0 0 0 0 0 0 1 4 0 1

Tat 0 0 0 0 0 0 2 1 3 0

Thun 0 1 0 0 0 0 0 0 0 5

Neural Networks

Nodes 20 22 24 36 40 42 46 48 50 52

Validation 53 47 13 41 55 70 40 40 45 72

Testing 5 13 15 10 18 12 6.7 10 10 10

Contributions

• Implemented pattern classification algorithms (Matlab)

• Analyzed recognition rates with varying parameters

• Explored a systematic way to perform classification

Future Directions

• Vibration sensors

• More recordings

• Timing (multiple frames, HMM)

• Real-time

• Continuous strokes

• Integrate context (rhythmic patterns)