Music Representation for Analysis using Data Mining

Music analysis, i.e. using computers to analyze fully notated pieces of musical score, is one of the most important research issues in computer music. Machine learning has played a crucial role in the computer music almost since its beginning. Recently, research in the field has focused on music mining. Data Mining is an emerging knowledge discovery process of extracting previously unknown, actionable information from very large scientific and commercial databases. Classification, clustering and association are the most well known data mining techniques. Data mining techniques are good candidates for music analysis. However, a proper music representation scheme is a prerequisite for their application. In this paper, we propose such a scheme for monophonic music representation as traditional data sets suitable for common data mining algorithms. We also present experimental results that demonstrate how the proposed representation technique is useful and helpful for analyzing and understanding music.