Representation and Discovery of Vertical Patterns in Music
ICMAI '02 Proceedings of the Second International Conference on Music and Artificial Intelligence
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
True suffix tree approach for discovering non-trivial repeating patterns in a music object
Multimedia Tools and Applications
Discovering nontrivial repeating patterns in music data
IEEE Transactions on Multimedia
Incremental mining of closed inter-transaction itemsets over data stream sliding windows
Journal of Information Science
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Mining repeating patterns from music data is one of the most interesting issues of multimedia data mining. However, less work are proposed for mining polyphonic repeating patterns. Hence, two efficient algorithms, A-PRPD (Apriori-based Polyphonic Repeating Pattern Discovery) and T-PRPD (Tree-based Polyphonic Repeating Pattern Discovery), are proposed to discover polyphonic repeating patterns from music data. Furthermore, a bit-string method is developed for improving the efficiency of the proposed algorithms. Experimental results show that the proposed algorithms, A-PRPD and T-PRPD, are both effective and efficient methods for mining polyphonic repeating patterns from synthetic music data and real data.