Hierarchical audio content classification system using an optimal feature selection algorithm
This paper proposes a hierarchical time-efficient method for audio classification and also presents an automatic procedure to select the best set of features for audio classification using Kolmogorov-Smirnov test (KS-test). The main motivation for our study is to propose a framework of general genre (e.g., action, comedy, drama, documentary, musical, etc...) movie video abstraction scheme for embedded devices-based only on the audio component. Accordingly simple audio features are extracted to ensure the feasibility of real-time processing. Five audio classes are considered in this paper: pure speech, pure music or songs, speech with background music, environmental noise and silence. Audio classification is processed in three stages, (i) silence or environmental noise detection, (ii) speech and non-speech classification and (iii) pure music or songs and speech with background music classification. The proposed system has been tested on various real time audio sources extracted from movies and TV programs. Our experiments in the context of real time processing have shown the algorithms produce very satisfactory results.
audio classification audio content analysis general genre video