Similarity matrix processing for music structure analysis

The structure analysis of pop and rock songs from audio signals is conducted via similarity matrix processing in this work. The similarity matrix offers pairwise similarity between any two short intervals of fixed length in a song. We use two similarity matrices to show their diverse characteristics. The characteristics are explained by musical chord successions. Then, several similarity matrix processing techniques are developed for music structure analysis. First, an algorithm is proposed to check the boundaries and periods of repetitive chord successions with short periods. Second, the Viterbi algorithm is applied to detect straight segments in sub-diagonal lines of the similarity matrix. Periods of repeating chord successions are used to refine the state space to enhance the detection performance. Furthermore, a post-processing technique is used to map detected segments into sections in a song. Experimental results from test musical audio data are given to demonstrate the performance of the proposed method.