The SVM Binary Tree Classification Using MRMR and F-Score Feature Selection Algorithms

Abstract

The discrimination between various types of speech and non-speech signals in audio data stream is the fundamental step for further indexing and retrieving. This paper considers some of the basic problems in audio content classification which is the key component in automatic audio retrieval system. It illustrates a potential use of statistical learning algorithm called support vector machine (SVM) for broadcast news (BN) audio classification task. The overall classification architecture uses binary tree SVM (BT-SVM) decision scheme in combination with well known audio features such as, MFCCs and low level MPEG-7 audio descriptors. The important step in creating such classification system is to define the optimal features for each binary SVM classifier. There exist various feature selection algorithms that help to create such feature set. Therefore we decided to implement F-score and Minimum Redundancy Maximum Relevance (MRMR) feature selection algorithms, as an effective search algorithms used in many pattern recognition tasks.