Adaptive interface for mapping body movements to sounds
(2018) 7th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMUSART 2018 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10783 LNCS. p.194-205- Abstract
Contemporary digital musical instruments allow an abundance of means to generate sound. Although superior to traditional instruments in terms of producing a unique audio-visual act, there is still an unmet need for digital instruments that allow performers to generate sounds through movements in an intuitive manner. One of the key factors for an authentic digital music act is a low latency between movements (user commands) and corresponding sounds. Here we present such a low-latency interface that maps the user’s kinematic actions into sound samples. The interface relies on wireless sensor nodes equipped with inertial measurement units and a real-time algorithm dedicated to the early detection and classification of a variety of... (More)
Contemporary digital musical instruments allow an abundance of means to generate sound. Although superior to traditional instruments in terms of producing a unique audio-visual act, there is still an unmet need for digital instruments that allow performers to generate sounds through movements in an intuitive manner. One of the key factors for an authentic digital music act is a low latency between movements (user commands) and corresponding sounds. Here we present such a low-latency interface that maps the user’s kinematic actions into sound samples. The interface relies on wireless sensor nodes equipped with inertial measurement units and a real-time algorithm dedicated to the early detection and classification of a variety of movements/gestures performed by a user. The core algorithm is based on the approximate inference of a hierarchical generative model with piecewise-linear dynamical components. Importantly, the model’s structure is derived from a set of motion gestures. The performance of the Bayesian algorithm was compared against the k-nearest neighbors (k-NN) algorithm, which showed the highest classification accuracy, in a pre-testing phase, among several existing state-of-the-art algorithms. In almost all of the evaluation metrics the proposed probabilistic algorithm outperformed the k-NN algorithm.
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- author
- Marković, Dimitrije and Malešević, Nebojša LU
- organization
- publishing date
- 2018-01-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Computational Intelligence in Music, Sound, Art and Design - 7th International Conference, EvoMUSART 2018, Proceedings
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- volume
- 10783 LNCS
- pages
- 12 pages
- publisher
- Springer
- conference name
- 7th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMUSART 2018
- conference location
- Parma, Italy
- conference dates
- 2018-04-04 - 2018-04-06
- external identifiers
-
- scopus:85044664926
- ISSN
- 0302-9743
- 1611-3349
- ISBN
- 9783319775821
- DOI
- 10.1007/978-3-319-77583-8_13
- language
- English
- LU publication?
- yes
- id
- aed11846-36cf-47a0-b41f-15d6aa26ceaa
- date added to LUP
- 2018-04-12 13:24:00
- date last changed
- 2024-06-24 12:59:47
@inproceedings{aed11846-36cf-47a0-b41f-15d6aa26ceaa,
abstract = {{<p>Contemporary digital musical instruments allow an abundance of means to generate sound. Although superior to traditional instruments in terms of producing a unique audio-visual act, there is still an unmet need for digital instruments that allow performers to generate sounds through movements in an intuitive manner. One of the key factors for an authentic digital music act is a low latency between movements (user commands) and corresponding sounds. Here we present such a low-latency interface that maps the user’s kinematic actions into sound samples. The interface relies on wireless sensor nodes equipped with inertial measurement units and a real-time algorithm dedicated to the early detection and classification of a variety of movements/gestures performed by a user. The core algorithm is based on the approximate inference of a hierarchical generative model with piecewise-linear dynamical components. Importantly, the model’s structure is derived from a set of motion gestures. The performance of the Bayesian algorithm was compared against the k-nearest neighbors (k-NN) algorithm, which showed the highest classification accuracy, in a pre-testing phase, among several existing state-of-the-art algorithms. In almost all of the evaluation metrics the proposed probabilistic algorithm outperformed the k-NN algorithm.</p>}},
author = {{Marković, Dimitrije and Malešević, Nebojša}},
booktitle = {{Computational Intelligence in Music, Sound, Art and Design - 7th International Conference, EvoMUSART 2018, Proceedings}},
isbn = {{9783319775821}},
issn = {{0302-9743}},
language = {{eng}},
month = {{01}},
pages = {{194--205}},
publisher = {{Springer}},
series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
title = {{Adaptive interface for mapping body movements to sounds}},
url = {{http://dx.doi.org/10.1007/978-3-319-77583-8_13}},
doi = {{10.1007/978-3-319-77583-8_13}},
volume = {{10783 LNCS}},
year = {{2018}},
}