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Music Perception and Cognition

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  1. Music Perception and Cognition 2009-2010 Perfecto Herrera perfecto.herrera@upf.edu

  2. About Perfe • Member of the MTG since 1995 (studio engineer, research support, research management, information brokering) • More than 80 papers on topics mostly related to Music Information Research (in the top-20 cited authors in this field) • Background studies: Cognitive Science, Audio recording and post-production, Computer Music • Topics of interest: too many  • Main expertises: machine learning, statistics, MPC • Full-time professor and Head of the Dept. of Sonology in ESMUC, teaching Music Technology, Psychoacoustics, Seminar of Sonology • Other interests: vintage synthesizers, polar explorers, electronic & experimental music, science-fiction, gamelan

  3. Music Perception and Cognition Web page http://mtg.upf.edu/~perfe/cursos/mpc Course wiki http://iua-share.upf.edu/wikis/smc/Music_Perception_and_Cognition • Let’s listen to some music as a starter • Methodology • Calendar • Assignments • Resources

  4. Let’s listen to some music What/How/Why do these examples challenge, illustrate, or sabotage our music perception/cognition mechanisms? • Paul Lansky – Iddle Chatter • Louis Couperin – Pavane en Fa Dièse Mineur • Pedro Guerrero – Dí, Perra Mora • Peter Brainer and his Baroque Orchestra – She Loves You • David Hykes – Diphonic self-counterpoint • Jean Claude Risset - Fall (from Computer Suite for Little Boy) • Diana Deutsch – Cambiata Illusion • Pink Floyd – Sheep • Aphex Twin - Omgyjya switch 7 • Lamonte Young - The second dream of the high-tension line stepdown transformer from the four dreams of china • Peter Schikele - New horizons in music appreciation Beethoven

  5. How would you face the understanding of a blue box that came from outer space?

  6. How can we “reverse engineer” the musical brain? • Strategy A: Let’s dismantle the brain into its basic components • Strategy B: Let’s treat it as a black-box: study how the output is affected by the input • In any case: we need a “theory” about the operations it performs • Why should you reverse-engineer the brain? • http://www.engineeringchallenges.org/cms/8996/9109.aspx

  7. David Marr’s 3 levels of explanation • Computational theory (problem definition): What is the goal of the computation, why is it appropriate, and what is the logic of the strategy by which it can be carried out? • Representation and algorithm (specific strategy): How can this computational theory be implemented? In particular, what is the representation for the input and output, and what is the algorithm for the transformation? (strategy B, previous slide) • Hardware implementation (implementation): How can the representation and algorithm be realized physically? (strategy A, previous slide)

  8. Object of MPC Which are the questions to be addressed by research on music perception and cognition? • From the computational theory level • From the functional level • From the implementational level Let’s write a list in the whiteboard!

  9. Broad Definition of Music Cognition • Music cognition addresses the mental activities involved in • Perceiving • Learning • Remembering • Producing MUSIC

  10. Perception vs. Cognition • Perception: transduction, basic sensations • Bottom-up (?), automatic (?), encapsulated • Cognition: musical knowledge • Top-down (?), consciousness-mediated (?), distributed BUT… • Bottom-up / Top-down distinction has been challenged by brain-cochlear connections and cortext-to-thalamus downward connections • Automatic / voluntary distinction has been challenged by research on implicit learning and knowledge

  11. A graphical modelfrom Peretz i Coltheart (2003), Modularity of music processing, Nature Neuroscience, 6(7).

  12. Why studying MPC in the SMC program? • The human listener is in the center of many SMC challenges (see the SMC roadmap document) • Users, composers, performers and players do what they do because of the processes and structures we are going to study here • Reverse-engineering the musical brain is needed in order to capture, mimic or approximate human features in artificial music systems • It is a source of insights when addressing music technology development • Sooner or later you will have to do some evaluation of subjective issues (synthesis technique, quality of processing, goodness of music description)… • Your turn to add reasons: --- --- ---