Mining polyphonic repeating patterns from music data using bit-string based approaches

Abstract

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.