tabla strokes recognition
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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)