Impress: A Machine Learning Approach to Soundscape Affect Classification for a Music Performance Environment

Miles Thorogood, and Philippe Pasquier

Proceedings of the International Conference on New Interfaces for Musical Expression

Abstract:

Soundscape composition in improvisation and performance contexts involves manyprocesses that can become overwhelming for a performer, impacting on thequality of the composition. One important task is evaluating the mood of acomposition for evoking accurate associations and memories of a soundscape. Anew system that uses supervised machine learning is presented for theacquisition and realtime feedback of soundscape affect. A model of sound-scape mood is created by users entering evaluations of audio environmentsusing a mobile device. The same device then provides feedback to the user ofthe predicted mood of other audio environments. We used a features vector ofTotal Loudness and MFCC extracted from an audio signal to build a multipleregression models. The evaluation of the system shows the tool is effective inpredicting soundscape affect.

Citation:

Miles Thorogood, and Philippe Pasquier. 2013. Impress: A Machine Learning Approach to Soundscape Affect Classification for a Music Performance Environment. Proceedings of the International Conference on New Interfaces for Musical Expression. DOI: 10.5281/zenodo.1178674

BibTeX Entry:

  @inproceedings{Thorogood2013,
 abstract = {Soundscape composition in improvisation and performance contexts involves manyprocesses that can become overwhelming for a performer, impacting on thequality of the composition. One important task is evaluating the mood of acomposition for evoking accurate associations and memories of a soundscape. Anew system that uses supervised machine learning is presented for theacquisition and realtime feedback of soundscape affect. A model of sound-scape mood is created by users entering evaluations of audio environmentsusing a mobile device. The same device then provides feedback to the user ofthe predicted mood of other audio environments. We used a features vector ofTotal Loudness and MFCC extracted from an audio signal to build a multipleregression models. The evaluation of the system shows the tool is effective inpredicting soundscape affect.},
 address = {Daejeon, Republic of Korea},
 author = {Miles Thorogood and Philippe Pasquier},
 booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression},
 doi = {10.5281/zenodo.1178674},
 issn = {2220-4806},
 keywords = {soundscape, performance, machine learning, audio features, affect grid},
 month = {May},
 pages = {256--260},
 publisher = {Graduate School of Culture Technology, KAIST},
 title = {Impress: A Machine Learning Approach to Soundscape Affect Classification for a Music Performance Environment},
 url = {http://www.nime.org/proceedings/2013/nime2013_157.pdf},
 year = {2013}
}