Feature Selection and Stacking for Robust Discrimination of Speech, Monophonic Singing, and Polyphonic Music

In this work we strive to find an optimal set of acoustic features for the discrimination of speech, monophonic singing, and polyphonic music to robustly segment acoustic media streams for annotation and interaction purposes. Furthermore we introduce ensemble-based classification approaches within this task. From a basis of 276 attributes we select the most efficient set by SVM SFFS. Additionally relevance of single features by calculation of information gain ratio is presented. As a basis of comparison we reduce dimensionality by PCA. We show extensive analysis of different classifiers within the named task. Among these are Kernel Machines, Decision Trees, and Bayesian Classifiers. Moreover we improve single classifier performance by Bagging and Boosting, and finally combine strengths of classifiers by StackingC. The database is formed by 2,114 samples of speech, and singing of 58 persons. 1,000 Music clips have been taken from the MTV-Europe-Top-20 1980-2000. The outstanding discrimination results of a working real time capable implementation stress the practicability of the proposed novel ideas.