SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS Third issue of the SHOES NEWS, June 1994. THE ICMC SPECIAL FOOT-TAPPING SESSION ICMC's paper chair Steffen Brandorff did a great job in finding support for this topic in the form of time and space in the ICMC program. The result is a special chapter in the proceedings and a 80 minute special session dedicated to "foot-tapping". The papers that were accepted by the ICMC paper committee are: Richard Parncutt, McGill University, CANADA A Model of Beat Induction Accounting for Perceptual Ambiguity by Continuously Variable Parameters Peter Desain and Henkjan Honing, NICI, Nijmegen/University of Amsterdam, NETHERLANDS Rule-based models of initial beat induction and an analysis of their behavior Ed Large, Ohio State University, USA The Resonant Dynamics of Beat Tracking and Meter Perception David Rosenthal, Masataka Goto and Yoichi Muraoka, International Media Research Foundation, JAPAN Rhythm Tracking Using Multiple Hypotheses Neil P. McAngus Todd, University of Sheffield, ENGLAND An auditory model of rhythm perception Peter Desain and Henkjan Honing, NICI, Nijmegen/University of Amsterdam, NETHERLANDS Advanced issues in beat induction modeling: syncopation and expressive timing We think it will be an exciting session. The mechanical shoe has had its first testruns during our visit to CCRMA and we will continue implementing new models to control it. Below are the ICMC paper abstracts submitted by the authors as contributions to this newsletter and the full list of references (from previous newsletters, plus new ones including the references referred to in the ICMC papers). The next SHOES NEWS will be about test sets for beat induction models and can be expected in the beginning of August. SHOES NEWS contributions, requests and questions can be sent to desain@nici.kun.nl THE ABSTRACTS Title : Rhythm Tracking Using Multiple Hypotheses Authors : David Rosenthal, Masataka Goto and Yoichi Muraoka We briefly describe two different rhythm-tracking systems, called, respectively, Machine Rhythm and BTS. Given a MIDI stream as input, Machine Rhythm produces an interpretation that is essentially isomorphic to the rhythmic information represented in normal musical notation. The output of the program defines the placement of measures and assigns rhythmic values (half-note beats, quarter-note beats, etc.) to each note. Although Machine Rhythm is not a real-time system, it processes the MIDI information sequentially, paving the way for possible future real- time implementations. The program attempts to handle some of the more sophisticated rhythm-tracking operations of which humans are capable, such as changes from duple to triple meter or changes in tempo. BTS tracks beats using raw audio signals as input -- in general, a much more difficult problem than tracking it from MIDI data. BTS accomplishes this task by leveraging the fact that for a large corpus of music -- rock and pop songs -- the beat is indicated with some reliability by the bass and snare drums. The fact that BTS does not rely on MIDI data enables it to handle a broad range of multimedia applications for which MIDI-based beat-tracking programs cannot be used, and the fact that it works in real time enables its application in a variety of live performance situations. Although the two systems differ in a number of respects, the systems use a similar strategy for managing uncertain or noisy input data -- namely, the strategy of pursuing multiple hypotheses. Title : A model of beat induction accounting for perceptual ambiguity by continuously variable parameters Author : Richard Parncutt A number of influential models of rhythmic pulse perception (e.g., Povel & Essens, 1985; Longuet-Higgins & Lee, 1982) have been based on discrete variables and yes-no decisions. In the present paper, a quantitative model of rhythm perception will be outlined in which all perceptual variables are assumed a priori to be continuous, helping the model to account in a realistic way for observed ambiguities and multiplicities in rhythmic responses. Each continuous variable in the model is initially understood as a probability - an estimate of the likelihood that an "average" listener will notice or become aware of a given rhythmic percept or structure. In cases of multiple concurrent percepts, continuous variables reflect the relative perceptual importances of the percepts. Continuous variables in the model include the phenomenal accent of individual sound events, and the likelihood of specific foot- tapping responses. The latter are called the "salience of pulse sensations" or the "strength of rhythmic levels." Pulse saliences, which are strongest in the vicinity of a moderate tempo of approximately 100 foot-taps per minute, are derived from the phenomenal accents by a pattern-matching procedure. Phenomenal accents, in turn, are enhanced (again, by continuously variable functions) if they occur either near the start of the rhythm (primacy effect) or just before the time of observation (recency effect). The relative strength of metrical accents, and the relative likelihood of the perception of specific meters (3/4, 4/4, etc.), are derived directly from pulse saliences. Foot-tapping responses may be determined either by pulse or by meter saliences. The relative importance of pulse and meter for foot- tapping appears to depend on a combination of musical style and the musical sophistication of the foot-tapper. Title : The Resonant Dynamics of Beat Tracking and Meter Perception Author : Ed Large One of the basic processes that affords the perception of metrical structure is the ability to perceive and follow the beat of a piece of music. The apparent simplicity of this behavior, however, masks a notoriously complex aspect of the human response to musical rhythm. Computer simulation of human "beat-tracking" behavior has proved exceedingly difficult. Many of the problems for computer simulation stem from the fact that musicians never perform rhythms in a perfectly regular, or mechanical, fashion. Performers produce sound patterns that reveal both intentional and unintentional timing variability. While such timing variability rarely poses difficulty for human performers and listeners, it seems that once a computer program loses the beat, recovery is nearly impossible. This paper presents a new model of human beat-tracking behavior as a dynamic process in which the temporal organization of external musical events synchronizes, or entrains, a set of non-linearly coupled oscillators. I analyze the behavior of this model using geometric techniques, and demonstrate the model's ability to follow real musical performances in the face of timing variability. Implications for the categorical perception of temporal intervals and the perception of metrical structure are discussed. Title : An auditory model of rhythm perception Author : Neil P. McAngus Todd Title : Rule-based models of initial beat induction and an analysis of their behavior Author : Peter Desain and Henkjan Honing In this paper a family of rule-based beat induction models is described (Longuet-Higgins & Lee, 1982; Lee, 1985; Longuet-Higgins, 1994), and introduces a new analysis method that goes beyond evaluating the models on a small set of carefully chosen musical examples. Title : Advanced issues in beat induction modeling: syncopation and expressive timing Author : Peter Desain and Henkjan Honing This paper presents a theory of beat induction based on the notion of expectancy. It focuses on important characteristics of beat induction that seem to be elegantly captured by the model: the possibility of syncopation and the dependency of the induced beat on global tempo and expressive timing. THE REFERENCES Allen, P. & R. Dannenberg (1990) Tracking musical beats in real time. Proceedings of the 1990 ICMC, 140-143. Bamberger, J. (1980) Cognitive structuring in the apprehension of simple rhythms. Archives de Psychologie, 48:171-199. Boulanger, R. (1990) Conducting the MIDI Orchestra, Part 1: Interviews with Max Mathews, Barry Vercoe and Roger Dannenberg. Computer Music Journal, 14(2). Bregman, A. (1990) Auditory Scene Analysis. The MIT Press, Cambridge. Brown, J.C. (1993) Determination of meter of musical scores by autocorrelation. J. Acoust. Soc. Am., 94(4), 1953-1957. Chafe, C. , B. Mont-Reynaud and L. Rush (1989) Toward an Intelligent Editor of Digital Audio: Recognition of Musical Constructs. In C. Roads (ed.), The Music Machine. MIT Press, Cambridge, MA. Chung, J. (1989) An Agency for the Perception of Musical Beats, M.S. Thesis, Massachusetts Institute of Technology. Clarke, E. (1985) Structure and expression in rhythmic performance. In P. Howell, I. Cross, and R. West (Eds.) Musical Structure and Cognition. London: Academic Press. Cooper, G. & Meyer, L. B. (1960) The rhythmic structure of music. Chicago: University of Chicago Press. Dannenberg, R. B. (1984) An on-line algorithm for real-time accompaniment. In Proceedings of the 1984 ICMC. Computer Music Association. Dannenberg, R.B. & B Mont-Reynaud (1987) Following an improvisation in real-time. In Proceedings of the 1987 International Computer Music Conference. 241 - 248. San Francisco: International Computer Music Association. Dannenberg, R.B. (1993) Music Understanding by Computer. In Proceedings of the IAKTA Workshop on Knowledge Technology in the Arts. 41-55. Osaka: IAKTA/LIST. Desain, P. & H. Honing (1989) Quantization of musical time: a connectionist approach. Computer Music Journal, 13:56-66. Desain, P. (1992) A (de)composable theory of rhythm perception. Music Perception, 9(4), 439-454. Desain, P., & Honing, H. (1992). The quantization problem: traditional and connectionist approaches. In M. Balaban, K. Ebcioglu, & O. Laske (eds.), Understanding Music with AI: Perspectives on Music Cognition. 448-463. Cambridge: MIT Press. Desain, P., & Honing, H. (1994). From foot-tapper systems to beat induction models. Proceedings ICMPC. Liege: ESCOM. Driesse, A. (1991) Real-time tempo tracking using rules to analyze rhythmic qualities, Proceedings of the 1991 ICMC, pp. 578-581. Fraisse P. (1982). Rhythm and tempo. In D Deutsch (Ed.), The Psychology of Music (pp. 149-180). New York: Academic. Glass, L. & Mackey, M. C. (1988) From clocks to chaos: The rhythms of life. Princeton, NJ: Princeton University Press. Goto, M. & Y. Hashimoto (1993) A distributed cooperative system to play MIDI instruments --- toward a remote session, IPSJ SIG Notes, Vol.93, No.109, 93-MUS-4-1. (in Japanese). Goto, M. & Y. Muraoka (1994) A real-time beat tracking system for musical acoustic signals, IPSJ SIG Notes, 94-MUS-7. (in Japanese, in press). Jackendoff, R. (1992) Musical processing and musical affect. In M. R. Jonesand S. Holleran (Eds.) Cognitive bases of musical communication. Washington: American Psychological Association. Jones, M. R. & Boltz, M. (1989) Dynamic Attending and Responses to Time. Psychological Review, 96, 459-491. Jones, M. R. (1976) Time, our lost dimension: Toward a new theory of perception, attention, and memory. Psychological Review, 83, 323-335. Jones, M. R., Boltz, M. & Kidd, G. (1982) Controlled attending as a function of melodic and temporal context. Journal of Experimental Psychology: Human Perception & Performance, 7, 211-218. Katayose, H., H. Kato, M. Imai, and S. Inokuchi (1989) An approach to an artificial music expert, Proceedings of the 1989 ICMC, pp.139-146. Kelso, J. A. S. & deGuzman, G. C. (in press) Order in time: How the cooperation between the hands informs the design of the brain. In H. Haken (Ed.) Natural and Synergetic Computers. Berlin: Springer- Verlag. Large, E. W. & Kolen, J. F. (in press) Resonance and the perception of musical meter. Connection Science. Lee, C. S. (1985) The rhythmic interpretation of simple musical sequences: towards a perceptual model. In R. West, P. Howell, & I. Cross (eds.) Musical Structure and Cognition. 53-69. London: Academic Press. Lee, C.S. (1991). The perception of metrical structure: Experimental evidence and a model. In P. Howell, R. West, & I. Cross (Eds.), Representing musical structure (pp. 59-127). London: Academic. Lerdahl, F., & Jackendoff, R. (1983). A generative theory of tonal music. Cambridge, MA: MIT Press. Longuet-Higgins, H.C. & C.S. Lee (1982) Perception of musical rhythms. Perception. 11, 115-128. Longuet-Higgins, H.C. (1976) The Perception of Melodies Nature 263: 646-653. Also in Longuet-Higgins, 1987. Longuet-Higgins, H.C. (1994) Unpublished computer program in POP-11, describing an algorithm named “shoeİ. Longuet-Higgins, H.C.(1987). Mental Processes. Cambridge, Mass.:MIT Press. McAuley, J. D. (1994) Finding metrical structure in time. In M. C. Mozer, P. Smolensky, D. S. Touretsky, J. L. Elman & A. S. Weigend (Eds.) Proceedings of the 1993 Connectionist Models Summer School. Hillsdale, NJ: Erlbaum Associates. Miller, B. O., D. L. Scarborough, & J. A. Jones (1992) On the perception of meter. In M. Balaban, K. Ebcioglu, & O. Laske (eds.), Understanding Music with AI: Perspectives on Music Cognition. 428- 447. Cambridge: MIT Press. Page, M. P. A. (1994) Modelling aspects of music perception using self-organizing neural networks. Unpublished doctoral dissertation, University of Exeter. Palmer, C. and C.L. Krumhansl (1990) Mental representations of musical meter. Journal of Experimental Psychology: Human Perception and performance 16(4), 728-741. Parncutt, R. (1987). The perception of pulse in musical rhythm. In A. Gabrielsson (Ed.), Action and Perception in Rhythm and Music (pp. 127-138). Stockholm: Royal Swedish Academy of Music. Parncutt, R. (1994). A perceptual model of pulse salience and metrical accent in musical rhythms. Music Perception, 11, 409-464. Pennycook, B., D.R. Stammen, & D. Reynolds (1993) Toward a computer model of a jazz improviser. In Proceedings of the 1993 International Computer Music Conference. 228-231. San Francisco: International Computer Music Association. Povel, D.J. & P. Essens (1985). Perception of temporal Patterns. Music Perception. 2(4):411-440 Rosenthal, D. (1988) "A Model of the process of listening to simple rhythms" proceedings ICMC 1988, 189 - 197. Rosenthal, D. (1992) Intelligent rhythm tracking. In Proceedings of the 1992 International Computer Music Conference. 227-230. San Francisco: International Computer Music Association. Rosenthal, D. (1992) Machine Rhythm: Computer Emulation of Human Rhythm Perception, Ph.D. Thesis, Massachusetts Institute of Technology Rosenthal, D. (1992). Emulation of human rhythm perception. Computer Music Journal, 16 (1), 64-76. Rowe, R. (1993). Interactive Music Systems: Machine Listening and Composing. MIT press: Cambridge. Schloss, W. A. (1985 On The Automatic Transcription of Percussive Music --- From Acoustic Signal to High-Level Analysis, Ph.D. Thesis, CCRMA, Stanford University. Schmidt, R. C., Beek, P. J., Treffner, P. J. & Turvey, M. T. (1991) Dynamical substructure of coordinated rhythmic movements. Journal of Experimental Psychology: Human Perception & Performance, 17, 635 - 651. Schroeder, M. (1991) Fractals, Chaos, Power Laws. New York: W. H. Freeman and Company. Schulze, H. H. (1989) The perception of temporal deviations in isochronic patterns. Perception & Psychophysics, 45, 291-296. Shaffer, L. H. (1981) Performances of Chopin, Bach, and Bartok: Studies in motor programming. Cognitive Psychology, 13, 326-376. Shaffer, L. H., Clarke, E. & Todd, N. P. M. (1985) Meter and rhythm in piano playing. Cognition, 20, 61-77. Shaw, M. and H. Coleman (1960) National Anthems of the World. London: Pitman. Sloboda, J. (1983) The communication of musical metre in piano performance. Quarterly Journal of Experimental Psychology 35. Todd, N. P. M. (1994) The auditory “primal sketchİ: A multi-scale model of rhythmic grouping. Journal of New Music Research, 23(1). Treffner, P. J. & Turvey, M. T. (1993) Resonance constraints on rhythmic movement. Journal of Experimental Psychology: Human Perception & Performance, 19, 1221-1237. Vercoe, B. (1985) The synthetic performer in the context of live music. In Proceedings of the 1985 International Computer Music Conference.199-200. San Francisco: Computer Music Association. Yeston, M. (1976) The stratification of musical rhythm. New Haven: Yale University Press. SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS SHOES NEWS