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This paper presents a hybrid approach for audio segmentation, in which the metric-based segmentation with long sliding windows is applied first to segment an audio stream into shorter sub-segments, and then the divide-and-conquer segmentation is applied to a fixed-length window that slides from the beginning to the end of each sub-segment to sequentially detect the remaining acoustic changes. The experimental results on five one-hour broadcast news shows show that our approach outperforms the existing metric-based and model-selection-based approaches.