BIC-based audio segmentation by divide-and-conquer

Audio segmentation has received increasing attention in recent years for its potential applications in automatic indexing and transcription of audio data. Among existing audio segmentation approaches, the BIC-based approach proposed by Chen and Gopalakrishnan is most well-known for its high accuracy. However, this window-growing-based segmentation approach suffers from the high computation cost. In this paper, we propose using the efficient divide-and-conquer strategy in audio segmentation. Our approaches detect acoustic changes by recursively partitioning an analysis window into two sub-windows using DeltaBIC. The results of experiments conducted on the broadcast news data demonstrate that our approaches not only have a lower computation cost but also achieve a higher segmentation accuracy than window-growing-based segmentation.