Music Summary Detection with State Space Embedding and Recurrence Plot

摘要

Automatic music summary detection is a task that identifies the most representative part of a song, facilitating users to retrieve the desired songs. In this paper, we propose a novel method based on state space embedding and recurrence plot. Firstly, an extended audio feature with state space embedding is extracted to construct a similarity matrix. Compared with the raw audio features, this extended feature is more robust against noise. Then recurrence plot based on global strategy is adopted to detect similar segment pairs within a song. Finally, we proposed to extract the most repeated part as a summary by selecting and merging the stripes containing the lowest distance in the similarity matrix under the constraints of slope and duration. Experimental results show that the performance of the proposed algorithm is more powerful than the other two competitive baseline methods.