Audio Coding Improvement Using Evolutionary Speech/Music Discrimination

Abstract

Automatic speech/music discrimination is an important tool used in many multimedia applications, becoming a research topic of interest in the last years. This paper presents our last works in the speech/music discrimination field, aiming to improve the coding efficiency of standard audio coders (i.e. MP3, AAC) when speech and music signals are involved. In order to discriminate between speech and music, a fuzzy rules-based expert system is incorporated into the decision-taking stage of traditional speech/music discrimination systems. The knowledge base of the fuzzy expert system has been obtained by means of a typical genetic learning algorithm (the Pittsburgh algorithm). The proposed speech/music discrimination scheme manages the operation of an intelligent audio coder, which selects a GSM coder for speech frames and an AAC coder for music ones, resulting in a lower bit rate regarding the case of using a standardized audio coder (AAC in this work). Further, the intelligent audio coder has been designed aiming to obtain a similar subjective audio quality than AAC. GSM operates at 13 kbits/s, while in the experiments the bit rate specification for AAC has been 32 kbits/s for one-channel audio signals.