Music segmentation and summarization based on self-similarity matrix

In this paper, we propose a new method for segmenting and summarizing music based on its structure analysis. To do that, we first extract timbre feature from acoustic music signal and construct a self-similarity matrix that shows similarities among the features within music clip. We then determine candidate boundaries for music segmentation by tracking standard deviation in the matrix. Similar segments such as repetition in music clip are clustered and merged. In this way, each music clip can be represented by a sequence of states where each state represents a music segment with similar feature. We assume that the longest segment of a music clip represents the music and hence use it as a summary of the music clip. We show the performance of our proposed method through experiments.