  The paper attempts to validate the effectiveness of tree classifiers to
classify tabla strokes especially the ones which are overlapping in nature. It
uses decision tree, ID3 and random forest as classifiers. A custom made data
sets of 650 samples of 13 different tabla strokes were used for experimental
purpose. 31 different features with their mean and variances were extracted for
classification. Three data sets consisting of 21361, 18802 and 19543 instances
respectively were used for the purpose. Validation has been done using measures
like ROC curve and accuracy. The experimental results showed that all the
classifiers showing excellent results with random forest outperforming the
other two. The effectiveness of random forest in classifying strokes which are
overlapping in nature is done by comparing the known results of that with
multi-layer perceptron.
