A Study on Feature Selection and Classification Techniques of Indian Music

AbstractIn this paper we present the effect of four feature selection algorithms namely genetic algorithm, Forward feature selection, information gain and correlation based on four different classifiers (Decision tree C4.5, K-Nearest neighbors, neural network and support vector machine). The feature sets used in this paper are extracted features from the preprocessed songs using MIR Toolbox in MATLAB, which encompass rhythm based, timbre based, pitch based, tonality based and dynamic features. Feature vectors are extracted from music segments from first 30 seconds and last thirty seconds of the music signal (time-decomposition). Experiments were carried out on the three dominant genres of Indian music: Carnatic, Hindustani and Bollywood. Our dataset is small with 290 songs, trimmed to extract the first and the last 30 second percepts. As pure Carnatic and Hindustani music being more prevalent in traditional settings, have limited work done to make their digital copies available but the collection of music we have used consists of songs of some of the most profound singers contributing to each of these genres. For high-dimensional feature sets, the feature selection provides a compact but discriminative feature subset which has an interesting trade-off between classification accuracy and computational effort. The experimental results have shown that the common features selected by each of the feature selection algorithms with respect to classifiers and percentage of classification accuracies for all the classification algorithms. Furthermore, it can be observed from our experiment that information gain based feature selection gives better and consistent accuracies than other feature selection algorithms and Neural network and SVM classifiers are the best suited classifiers for Indian Song dataset