Segmentation and Recognition of Tabla Strokes

A system that segments and labels tabla strokes from real performances is described. Performance is evaluated on a large database taken from three performers under different recording conditions, containing a total of 16,834 strokes. The current work extends previous work by Gillet and Richard (2003) on categorizing tabla strokes, by using a larger, more diverse database that includes their data as a benchmark, and by testing neural networks and treebased classification methods. First, the time-domain signal was segmented using complex-domain thresholding that looked for sudden changes in amplitude and phase discontinuities. At the optimal point on the ROC curve, false positives were less than 1% and false negatives were less than 2%. Then, classification was performed using a multivariate Gaussian model (mv gauss) as well as non-parametric techniques such as probabilistic neural networks (pnn), feed-forward neural networks (ffnn), and tree-based classifiers. Two evaluation protoco...

• Type: Conference
• Year: 2005
• Where: ISMIR
• Authors: Parag Chordia
• Keywords: Information Retrieval, ISMIR 2005, Mv Gauss, Neural Networks, Tabla Strokes