A Method for Discovering Musical Patterns Through Time Series Analysis of Symbolic Data

Tamar C. Adler-Berman
Northwestern University, IL, USA (June, 2006)


This paper proposes a method for discovering musical patterns through the conversion of MIDI files into time series and analyzing these with data mining tools and SQL queries. The method was tested on patterns prevalent in the music of W.A. Mozart, as represented in a corpus of 505 MIDI sequences of pieces by Mozart.

The novelty of the pattern extraction method described here lies in its ability to discover and retrieve sequences that are composed of complex events. These contain both melodic and harmonic features, which may be overlaid upon each other, embedded within each other, and may be separated by, or occur simultaneously with, other patterns or occurrences.

Results support the feasibility of constructing an information system for the discovery and retrieval of complex musical patterns based on time series analysis. This system is intended for use by music researchers, scholars and students. A prototype of this system, tested and described in this paper, has discovered frequently recurring patterns, and provided examples of musical passages which contain a particular pattern selected for investigation.

This work demonstrates how data mining can be applied to musical time series to discover frequent musical sequences. Further, it shows how examples of a particular complex pattern - the 1-74-3 structure - can be fetched from the corpus based on the same time series representation.

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