Pattern discovery from audio recordings by Variable Markov Oracle: A music information dynamics approach

Abstract

In this paper, a framework for automatic pattern discovery within an audio recording is proposed. The concept of the proposed framework stems from music information dynamics and is realized by Variable Markov Oracle. Music information dynamics is the research area focusing on information theoretic measures describing musical structure and is thus closely related to the field of music pattern discovery. Variable Markov Oracle is a data structure that provides both fast retrieval of repeated sub-clips from a signal and efficient calculation of music information dynamics measures. Evaluation of the proposed framework is performed on the JKU Patterns Development Dataset with significantly improved performance of the current state of the art.