Heuristic approach for generic audio data segmentation and annotation

Abstract

A real-time audio segmentation and indexing scheme is presented in this paper. Audio recordings are segmented and classified into basic audio types such as silence, speech, music, song, environmental sound, speech with the music background, environmental sound with the music background, etc. Simple audio features such as the energy function, the average zero-crossing rate, the fundamental frequency, and the spectral peak track are adopted in this system to ensure on-line processing. Morphological and statistical analysis for temporal curves of these features are performed to show differences among different types of audio. A heuristic rule-based procedure is then developed to segment and classify audio signals by using these features. The proposed approach is generic and model free. It can be applied to almost any content-based audio management system. It is shown that the proposed scheme achieves an accuracy rate of more than 90% for audio classification. Examples for segmentation and indexing of accompanying audio signals in movies and video programs are also provided.