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Computational Music Structure Analysis

Dagstuhl Seminar 16092. Computational Music Structure Analysis. organized by. Meinard Müller, Elaine Chew, Juan Pablo Bello. 28.02.2016 – 04.03.2016. Stefan Balke (Universität Erlangen-Nürnberg, DE). Studied EE in Hannover Joined Meinard’s group in 2014

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Computational Music Structure Analysis

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  1. Dagstuhl Seminar 16092 Computational Music Structure Analysis organized by Meinard Müller, Elaine Chew, Juan Pablo Bello 28.02.2016 – 04.03.2016

  2. Stefan Balke(Universität Erlangen-Nürnberg, DE) • Studied EE in Hannover • Joined Meinard’s group in 2014 • Cross-modal retrieval scenarios • Structure analysis in jazz music

  3. Juan Pablo Bello(New York University, US)

  4. Frederic Bimbot(IRISA - Rennes, FR)

  5. Carmine Emanuele Cella(École normale supérieure - Paris, FR)

  6. Elaine Chew(Queen Mary University of London, GB) Operations Research Mathematical Models Optimisation/Algorithms Piano Performance Solo / chamber music Perception/Cognition Spiral Array Model Expressivity/Prosody Tipping Points Music Structures

  7. Ching-Hua Chuan(University of North Florida, US)

  8. Roger B. Dannenberg(Carnegie Mellon University, US) A new criterion for beat tracking: structural consistency

  9. Matthew Davies(INESC TEC – Porto, PT)

  10. Christian Dittmar(Universität Erlangen-Nürnberg, DE)

  11. Sebastian Ewert(Queen Mary University of London, GB) Musical Structure Temporal Segmentation Exploiting Structure in Music What about this range? Style-Specific Methods? Exposition Development Recapitulation Intro Verse Chorus Bridge Years since first method

  12. Mary Farbood(New York University, US)

  13. Masataka Goto(AIST – Ibaraki, JP) • Working on Music Information Research since 1992 • Music understanding technologies • Music interfaces (Active Music Listening Interfaces) • Singing information processing • At Dagstuhl, I’d like to discuss“Can We Reach a Consensus on the Minimum Amount of Originality to Regard a Piece of Music as Original?”

  14. DorienHerremans(Queen Mary University of London, GB) Music generation with tension profiles & patterns • Spiral array to model tension: • Cloud diameter • Cloud momentum • Cloud cosine distance • Tensile strain 2. Pattern detection (cosiatec) 3. Generation => Optimization approach to generate music into profiles/structures • Objective function based on tension profiles • Hard constraint: detected patterns • Solving: Metaheuristics

  15. Andre Holzapfel(OFAI-Wien, AT) Computational analysis of rhythm and meter in world’smusics: Flexible models, richersignal representations, thickdescriptions

  16. Rainer Kleinertz(Universität des Saarlandes, DE)

  17. Frank Kurth(Fraunhofer FKIE – Wachtberg, DE)

  18. Cynthia Liem(TU Delft, NL)

  19. Brian McFee(New York University, US)

  20. Meinard Müller(Universität Erlangen-Nürnberg, DE)

  21. Oriol Nieto(Pandora / New York University, US) MSAF http://github.com/urinieto/msaf

  22. Mitchell Ohriner [“oh-RYE-ner”](Shenandoah University, US) Studies: Teaches: flow in rap music harmony expressive timing form sensorimotor synchronization aural skills Black Thought (a.k.a. Tariq Trotter), “I Remember,” verse 1

  23. Hélène Papadopoulos(L2S – Gif sur Yvette, FR)

  24. GeoffroyPeeters(IRCAM – CNRS – IPMC, Paris, FR)

  25. Christopher Raphael(Indiana University – Bloomington, US)

  26. Martin Rohrmeier(TU Dresden, DE)

  27. Mark Sandler(Queen Mary University of London, GB)

  28. Xavier Serra(UPF – Barcelona, ES)

  29. Jordan Smith(AIST – Tsukuba, JP)

  30. Anja Volk(Utrecht University, NL)

  31. Christof Weiß(Universität Erlangen-Nürnberg, DE) 2 3 4 1 ? ? ? ? 2 3 4 1

  32. Geraint A. Wiggins(Queen Mary University of London, GB) Models of cognition that account for music, language & creativity in both Current work: IDyOT (Information Dynamics of Thinking) Cognitive architecture based around new (!?!) kind of symbolic statistical sequence learning method, based on Shannon IT, that generates its own symbols and gives them semantics in terms of learned perceptual grounding

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