Efficient sparse self-similarity matrix construction for repeating sequence detection

This paper presents an efficient way to construct the self-similarity matrix, a popular approach, to detect repeating segments in music. Our proposed method extends the sparse suffix tree construction algorithm to accept vectors as input to construct an initial selection of repeating sequences to generate a sparse self-similarity matrix. Our proposed insertion criterion does not only rely on vector-to-vector similarity but also measures the similarity between two subsequences in its insertion criteria. As such, our method is more robust as compared to approaches that simply quantize the input vectors into symbols for suffix tree construction. In addition, the proposed method is efficient in both computation and memory storage. Our experimental results showed that the proposed approach obtains similar average F1 score as compared to the traditional self-similarity approach with much less computational cost and memory usage.