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Perceptive Strategies in Computational Motivic Analysis: Why and How.

Perceptive Strategies in Computational Motivic Analysis: Why and How. Olivier.Lartillot@ircam.fr www.ircam.fr/equipes/repmus/lartillot. Perceptive Strategies in Computational Motivic Analysis: Why and How.

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Perceptive Strategies in Computational Motivic Analysis: Why and How.

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  1. Perceptive Strategiesin Computational Motivic Analysis:Why and How. Olivier.Lartillot@ircam.fr www.ircam.fr/equipes/repmus/lartillot

  2. Perceptive Strategiesin Computational Motivic Analysis:Why and How. The motivic dimension of music, still resisting to a complete and thorough explication, remains one of the most ambitious domains of interest of music analysis. Music semiology has inspired an ideal of “neutrality”, of the possibility of total independence of the structure to perceptual context. This paradigm has been questioned by competing tendencies that defend the need of a perceptual or even “cognitive” foundation of music analysis. Such dilemma finds a new resonance in today research in automatic musical pattern discovery, which may be considered as a computational inquiry of motivic analysis. Current limitations in this domain seem to stem from an insufficient consideration of the perceptual specificity of musical expression. We propose a general computational model that attempts to mimic music perception. This model relies on two main temporal characteristics of music: chronological direction and short-term selectivity. As a result, musical pattern is defined as an aggregation of successive local intervals. Patterns are induced by analogy between current context and similar past contexts that are reactivated through associative memory. Here, patterns are conceived of as concepts that are actualized in the musical score. This score is represented as a network of notes, which are linked to pattern occurrences that themselves form meta-patterns of patterns. This computational modelling, in process of development as an Open Music library called OMkanthus, aims at offering to musicology a detailed and explicit understanding of music, and suggesting to cognitive science the necessary conditions for musical pattern perception.

  3. Perceptive Strategiesin Computational Motivic Analysis:Why and How. Olivier.Lartillot@ircam.fr www.ircam.fr/equipes/repmus/lartillot

  4. Computational Motivic Analysis • Automated Music Analysis • Motivic Analysis • Rudolph Reti • Nicolas Ruwet: Paradigmatic Analysis • Musical Pattern Discovery • Exact Pattern • Dynamic Programming

  5. Dynamic Programming ACGGCGTTACGGCAGCGCTGATCGTATCTAGCTAGTCTATGCTAT ACGGCGTTACGAGCAGCGCTGATCGTATCTAGTAGTCTATGCGAT CDEFGFEADGAGFEF?

  6. Automated Music Analysis • Motivic Analysis • Rudolph Reti • Nicolas Ruwet: Paradigmatic Analysis • Musical Pattern Discovery • Exact Pattern • Dynamic Programming • Perceptual Model?

  7. Cognitive Constraints Cultural Knowledge Poietic Level Neutral Level Esthesic Level Music Semiology Immanent Structures? Score Composer Listener

  8. Immanent Structures? Bad patterns Good patterns Transcendent Structures!

  9. Automated Music Analysis • Motivic Analysis • Rudolph Reti • Nicolas Ruwet: Paradigmatic Analysis • Musical Pattern Discovery • Exact Pattern • Dynamic Programming • Perceptual Model

  10. Perceptive Strategiesin Computational Motivic Analysis:Why and How. Olivier.Lartillot@ircam.fr www.ircam.fr/equipes/repmus/lartillot

  11. Temporal Approach

  12. Temporal Approach

  13. Apprehensive Retention

  14. Apprehensive Retention

  15. Reproductive Remembering

  16. Objectivation

  17. Recognitive Remembering

  18. Recognitive Remembering

  19. Pattern Repetition

  20. Abstract Pattern

  21. Abstract Pattern Tree

  22. Pattern Occurrence Chain

  23. Parallel Patterns

  24. Architecture • loop for note in score • memorize new retentions • develop current expected occurrences • develop current unexpected occurrences • develop current objectivations • find new objectivations

  25. OMkanthus 0.1

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