Tutorial 2: Melodic Search and Pattern Discovery for Symbolic Music Information Retrieval
Supplementary material
The material on this page accompanies the tutorial presented by Ian Knopke (BBC) and Eric Nichols (Indiana University)
Recent discussion in the MIR community has led to a renewed interest in symbolic music resources. These representations, often created from traditional musical notation, incorporate a much higher degree of structured musical information. This, combined with their discrete nature makes it possible to discover a larger range of musically-relevant patterns that would otherwise be difficult to approach, especially if progress is to be made in musicologically-centered problems
The intended audience for this tutorial is researchers who have some familiarity with MIR concepts and background but may be new to symbolic music research. The focus of the first part is primarily on finding musical patterns and motifs in symbolic music collections. Various exact and approximate search techniques in a musical context will be discussed, as well as their application to large collections and the scaling issues that can arise. The second part examines how results from music cognition research have provided insight to classic music IR problems, focussing on cognitive approaches to the problems of pattern discovery in melody and the modeling of melodic expectation. Examples, demonstrations and visualizations will be given using the PerlHumdrum and PerlLilypond toolkits for working with Humdrum scores, and the Musicat computer model of musical listening.
Part 1: Pattern recognition and search (coming soon)
Part 2: Cognitive approaches to MIR
Search Techniques
Coming soon...
Music Cognition
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Ramirez, R., Perez, A. and Kersten, S. (2008). Performer Identification in Celtic Violin Recordings. ISMIR 2008.
Schellenberg, E. G. (1996). Expectancy in melody: Tests of the implication-realization model. Cognition, 58, 75-125.
Schellenberg, E. G. (1997). Simplifying the implication-realization model of melodic expectancy. Cognition, 58, 75-125.
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(more references coming soon...)
The authors would like to express their thanks for the generous support provided by their respective institutions: the BBC and Douglas Hofstadter's Center for Research on Concepts and Cognition at Indiana University. In addition, Mr. Nichols's research was supported by NSF CreativeIT grant #0738384.