An Optimization of Audio Classification and Segmentation using GASOM Algorithm

Abstract

Now-a-days, multimedia content analysis occupies an important place in widely used applications. It may depend on audio segmentation which is one of the many other tools used in this area. In this paper, we present an optimized audio classification and segmentation algorithms that are used to segment a superimposed audio stream according to its content into 10 main audio types: speech, non-speech, silence, male speech, female speech, music, environmental sounds, and music genres, such as classic music, jazz, and electronic music. We have tested the KNN, SVM, and GASOM algorithms on two audio classification systems. In the first audio classification system, the audio stream is discriminated into speech no-speech, pure-speech/silence, male speech/female speech, and music/ environmental sounds. However, in the second audio classification system, the audio stream is segmented into music/speech, pure-speech/silence, male speech/female speech. For pure-speech/silence discrimination, it is performed in the two systems according to a rule-based classifier. Concerning the music segments in both systems, they are discriminated into different music genres using the decision tree as a classifier. Also, the first audio classification system has succeeded to achieve higher performances compared to the second one. Indeed, in the first system using the GASOM algorithm with leave-one-out validation technique, the average accuracy has reached 99.17% for the music/environmental sounds discrimination. Moreover, in both systems, the GASOM algorithm has always reached the best results of performances compared to KNN and SVM algorithms. Therefore, in the first system, the GASOM algorithm has been contributed to obtain an optimized consumption time compared to that one obtained using the two HMM and MLP methods.