Improved Score-performance Matching Using Both Structural and Temporal Information from MIDI Recordings

Abstract Automated score-performance matching is a complex problem due to the use of expressive timing by performers and the presence of notes that are unspecified in the score, such as performance errors and ornaments. Automated matchers typically use performance data extracted from MIDI recordings. For the most part, these algorithms use structural information, such as pitch and chronological succession, but do not use timing information. As a result, most matchers cannot deal satisfactorily with ornamented performances or performances that exhibit extreme variations in tempo. The matcher presented here relies both on structural and temporal information, allowing it to generate an accurate match even for heavily ornamented performances. Hand-made score-performance matches on a corpus of 80 MIDI recordings of organ performances of two pieces were used as ground truth data for a comparison with matcher results. The matcher achieved a nearly perfect accuracy rate. In addition, the matcher performed equally well or better than matchers previously described in the literature on a set of piano performances of two pieces by Chopin, thus demonstrating its versatility and robustness. We also propose a heuristic for the identification of ornaments and errors that is based on perceptual principles, and which could theoretically be amenable to empirical study. Finally, this matcher is designed to accommodate multi-channel MIDI recordings of performances from keyboard instruments with multiple manuals, such as organ or harpsichord. This feature makes it a potentially valuable tool for the investigation of ensemble performances of MIDI instruments.

[1]  S. B. Needleman,et al.  A general method applicable to the search for similarities in the amino acid sequence of two proteins. , 1970, Journal of molecular biology.

[2]  Edwaed W. Large Dynamic programming for the analysis of serial behaviors , 1993 .

[3]  C. Palmer,et al.  Units of knowledge in music performance. , 1993, Journal of experimental psychology. Learning, memory, and cognition.

[4]  C. Palmer Mapping musical thought to musical performance. , 1989, Journal of experimental psychology. Human perception and performance.

[5]  B. Repp The Art of Inaccuracy: Why Pianists' Errors Are Difficult to Hear , 1996 .

[6]  Roger A. Kendall,et al.  The Communication of Musical Expression , 1990 .

[7]  B. Repp Patterns of note onset asynchronies in expressive piano performance. , 1996, The Journal of the Acoustical Society of America.

[8]  David Sankoff,et al.  Comparison of musical sequences , 1990, Comput. Humanit..

[9]  William Drabkin,et al.  Counterpoint: A Translation of 'Kontrapunkt' , 1989 .

[10]  Luke Windsor,et al.  Data processing in music performance research: Using structural information to improve score-performance matching , 2000, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[11]  Hank Heijink,et al.  Robust Score-Performance Matching: Taking Advantage of Structural Information , 1997, ICMC.

[12]  Yoshitaka Nakajima,et al.  Auditory Scene Analysis: The Perceptual Organization of Sound Albert S. Bregman , 1992 .

[13]  Luke Windsor,et al.  Make Me a Match: An Evaluation of Different Approaches to ScorePerformance Matching , 2000, Computer Music Journal.

[14]  Caroline Palmer,et al.  On the Assignment of Structure in Music Performance , 1996 .

[15]  Roger B. Dannenberg,et al.  An On-Line Algorithm for Real-Time Accompaniment , 1984, ICMC.

[16]  O. Gotoh An improved algorithm for matching biological sequences. , 1982, Journal of molecular biology.

[17]  Ruth Rasch,et al.  Synchronization in performed ensemble music , 1979 .

[18]  C. Palmer Music performance. , 1997, Annual review of psychology.

[19]  W Goebl,et al.  Melody lead in piano performance: expressive device or artifact? , 2001, The Journal of the Acoustical Society of America.

[20]  Christopher Raphael,et al.  Aligning music audio with symbolic scores using a hybrid graphical model , 2006, Machine Learning.

[21]  Miller Puckette,et al.  Score Following in Practice , 1992, ICMC.

[22]  William P. Birmingham,et al.  Following a Musical Performance from a Partially Specified Score , 2001 .

[23]  C Palmer,et al.  Range of planning in music performance. , 1995, Journal of experimental psychology. Human perception and performance.

[24]  Stephen McAdams,et al.  Communicating voice emphasis in harpsichord performance , 2009 .

[25]  Curtis Roads,et al.  The Computer Music Tutorial , 1996 .

[26]  Peter Desain,et al.  Tempo Curves Considered Harmful , 1991, ICMC.

[27]  Ichiro Fujinaga,et al.  Study of Expression and Individuality in Music Performance Using Normative Data Derived from MIDI Recordings of Piano Music , 2007 .

[28]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[29]  Leon van Noorden,et al.  Minimum differences of level and frequency for perceptual fission of tone sequences ABAB , 1977 .

[30]  C. Chuan Tone and Voice: A Derivation of the Rules of Voice-Leading from Perceptual Principles , 2001 .

[31]  Nicola Orio,et al.  Robust Polyphonic Midi Score Following with Hidden Markov Models , 2004, ICMC.

[32]  Gerhard Widmer,et al.  MATCH: A Music Alignment Tool Chest , 2005, ISMIR.

[33]  Henkjan Honing POCO: An Environment for Analysing, Modifying and Generating Expression in Music , 1990, ICMC.

[34]  Jason D. Vantomme,et al.  Score Following by Temporal Pattern , 1995 .

[35]  Roger B. Dannenberg,et al.  New Techniques for Enhanced Quality of Computer Accompaniment , 1988, ICMC.

[36]  P. Desain,et al.  Music, Mind, and Machine: Studies in Computer Music, Music Cognition, and Artificial Intelligence , 1992 .

[37]  R. Jackendoff,et al.  A Generative Theory of Tonal Music , 1985 .

[38]  鐘期 坂本,et al.  Tonal Pitch Space を用いた楽曲の和声解析 , 2009 .