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On urban soundscape mapping: A computer can predict the outcome of soundscape assessments
RISE, SP – Sveriges Tekniska Forskningsinstitut.
2016 (English)In: Proceedings of the INTER-NOISE 2016 - 45th International Congress and Exposition on Noise Control Engineering: Towards a Quieter Future, 2016, 4725-4732 p.Conference paper, Published paper (Refereed)
Abstract [en]

The purpose of this study was to investigate whether or not a computer may predict the outcome of soundscape assessments, based on acoustic data only. It may be argued that this is impossible, because a computer lack life experience. Moreover, if the computer was able to make an accurate prediction, we also wanted to know what information it needed to make this prediction. We recruited 33 students (18 female; Mage = 25.4 yrs., SDage = 3.6) out of which 30 assessed how pleasant and eventful 102 unique soundscape excerpts (30 s) from Stockholm were. Based on the Bag of Frames approach, a Support Vector Regression learning algorithm was used to identify relationships between various acoustic features of the acoustics signals and perceived affective quality. We found that the Mel-Frequency Cepstral Coefficients provided strong predictions for both Pleasantness (R2 = 0.74) and Eventfulness (R2 = 0.83). This model performed better than the average individual in the experiment in terms of internal consistency of individual assessments. Taken together, the results show that a computer can predict the outcome of soundscape assessments, which is promising for future soundscape mapping. © 2016, German Acoustical Society (DEGA). All rights reserved.

Place, publisher, year, edition, pages
2016. 4725-4732 p.
Keyword [en]
Machine learning, Soundscape mapping, Urban planning, Acoustic variables control, Acoustics, Artificial intelligence, Forecasting, Learning algorithms, Learning systems, Mapping, Speech recognition, Accurate prediction, Acoustic features, Internal consistency, Life experiences, Mel frequency cepstral co-efficient, Perceived affective qualities, Soundscapes, Support vector regression (SVR), Acoustic noise
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-27615Scopus ID: 2-s2.0-84994639413OAI: oai:DiVA.org:ri-27615DiVA: diva2:1059673
Conference
45th International Congress and Exposition on Noise Control Engineering: Towards a Quieter Future, INTER-NOISE 2016, 21 August 2016 through 24 August 2016
Note

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Available from: 2016-12-22 Created: 2016-12-21 Last updated: 2016-12-22Bibliographically approved

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