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Towards more effective mapping strategies for digital musical instruments

Towards more effective mapping strategies for digital musical instruments. Patrick McGlynn Linux Audio Conference 2011 National University of Ireland, Maynooth. Commercial interest. ‘Implicit communication’. Designing a Digital Musical Instrument.

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Towards more effective mapping strategies for digital musical instruments

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  1. Towards more effective mapping strategies for digital musical instruments Patrick McGlynn Linux Audio Conference 2011 National University of Ireland, Maynooth

  2. Commercial interest ‘Implicit communication’

  3. Designing a Digital Musical Instrument 1. Decide upon the gestures which will control it 2. Define the gesture capture strategies which work best 3. Define the accompanying synthesis algorithms / music software 4. Map the sensor outputs to the music control 5. Decide on the feedback modalities available, apart from the sound itself (visual, tactile, kinaesthetic, etc.) New Digital Musical Instruments: Control and Interaction Beyond the Keyboard Miranda & Wanderley

  4. Mapping – an overview “An efficiency-focused approach to interaction may no longer suffice: it needs to be complemented by knowledge on the aesthetic aspects of the user experience” Easy doesn’t do it: skill and expression in tangible aesthetics Djajadiningrat, Matthews & Stienstra “A more holistic performance exploration of the parameter space” Radical User Interfaces for Real-Time Musical ControlHunt

  5. Mapping in electronic music 1. Convergent 2. Divergent

  6. Mapping in product design

  7. Mapping in product design Naturally-mapped controls are laid-out: “…in a manner spatially analogous to the layout of the devices they control” The Design of Future Things Norman

  8. Mapping in product design 1. Provide rich, complex, and natural signals 2. Be predictable 3. Provide a good conceptual model 4. Make the output understandable 5. Provide continual awareness, without annoyance 6. Exploit natural mappings to make interaction understandable The Design of Future Things Norman

  9. Systematic mapping Begins with… A careful look at the resources available…

  10. Complexity of performance data Mapping groups: A: Raw data B: Symbolic / semiotic data C: Gestural data

  11. Complexity of performance data Knowledge management: A: Data B: Information C: Knowledge

  12. Complexity of performance data Mapping groups: A: Raw data B: Symbolic / semiotic data C: Gestural data

  13. Degrees of freedom A mousepad is a two-dimensional surface with three degrees of freedom: X, Y, and QZ Motor Behaviour Models for Human-Computer Interaction MacKenzie

  14. Combining simple control data Motor Behaviour Models for Human-Computer Interaction MacKenzie

  15. Combining simple control data Motor Behaviour Models for Human-Computer Interaction MacKenzie

  16. Example application - DroneLab Four-oscillator additive synthesizer, with distortion and low/band-pass filters. A drone/noise machine.

  17. Example application - DroneLab (caution)

  18. Example application - DroneLab

  19. Example application - DroneLab On/off Group A: Raw data

  20. Example application - DroneLab Pitch/volume Group B: Symbolic / semiotic

  21. Example application - DroneLab Distortion Group C: Gestural

  22. Some thoughts… • The potential for a musical performance system to engage, challenge and stimulate the user depends upon how their actions are interpreted by the system. • Every performance action sends information into the system. • It is critical that we grasp all possible ways to interpret this data.

  23. Thanks for listening! Towards more effective mapping strategies for digital musical instruments Patrick McGlynn Linux Audio Conference 2011 National University of Ireland, Maynooth

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