Visualizing music and audio using self-similarity
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
A Space-Economical Suffix Tree Construction Algorithm
Journal of the ACM (JACM)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Repeating pattern discovery and structure analysis from acoustic music data
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Unsupervised pattern discovery for multimedia sequences
Unsupervised pattern discovery for multimedia sequences
Finding maximum-length repeating patterns in music databases
Multimedia Tools and Applications
Motif Search in DNA Sequences Using Generalized Suffix Tree
ICIT '07 Proceedings of the 10th International Conference on Information Technology
IEEE Transactions on Audio, Speech, and Language Processing
Discovering nontrivial repeating patterns in music data
IEEE Transactions on Multimedia
Hi-index | 0.00 |
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.