Speech and music classification in audio documents

To index efficiently the soundtrack of multimedia documents, it is necessary to extract elementary and homogeneous acoustic segments. In this paper, we explore such a prior partitioning which consists in detect the two basic components, which are speech and music components. The originality of this work is that music and speech are not considered as two classes and two classification systems are independently defined, a speech/non-speech one and a music/non-music one. This approach permits to better characterize and discriminate each component: in particular, two different feature spaces are necessary as two pairs of Gaussian mixture models. More, the acoustic signal is divided into four types of segments: speech, music, speech-music and other. The experiments are performed on the soundtracks of audio video documents (films, TV sport broadcasts). The performance proves the interest of this approach, so called the Differentiated Modeling Approach.