Preliminary investigation of the short-term in situ performance of an automatic masker selection system
Soundscape augmentation or ``masking'' introduces wanted sounds into the acoustic environment to improve acoustic comfort. Usually, the masker selection and playback strategies are either arbitrary or based on simple rules (e.g. –3 dBA), which may lead to sub-optimal increment or even redu...
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sg-ntu-dr.10356-1686692023-09-22T15:39:06Z Preliminary investigation of the short-term in situ performance of an automatic masker selection system Lam, Bhan Ong, Zhen-Ting Ooi, Kenneth Ong, Wen-Hui Wong, Trevor Gan, Woon-Seng Watcharasupat, Karn N. School of Electrical and Electronic Engineering 52nd International Congress and Exposition on Noise Control Engineering (Inter-Noise 2023) Digital Signal Processing Laboratory Science::Physics::Acoustics Engineering::Electrical and electronic engineering Soundscape Soundscape Augmentation Auditory Masking Park Perceived Affectie Quality Soundscape augmentation or ``masking'' introduces wanted sounds into the acoustic environment to improve acoustic comfort. Usually, the masker selection and playback strategies are either arbitrary or based on simple rules (e.g. –3 dBA), which may lead to sub-optimal increment or even reduction in acoustic comfort for dynamic acoustic environments. To reduce ambiguity in the selection of maskers, an automatic masker selection system (AMSS) was recently developed. The AMSS uses a deep-learning model trained on a large-scale dataset of subjective responses to maximize the derived ISO pleasantness (ISO 12913-2). Hence, this study investigates the short-term in situ performance of the AMSS implemented in a gazebo in an urban park. Firstly, the predicted ISO pleasantness from the AMSS is evaluated in comparison to the in situ subjective evaluation scores. Secondly, the effect of various masker selection schemes on the perceived affective quality and appropriateness would be evaluated. In total, each participant evaluated 6 conditions: (1) ambient environment with no maskers; (2) AMSS; (3) bird and (4) water masker from prior art; (5) random selection from same pool of maskers used to train the AMSS; and (6) selection of best-performing maskers based on the analysis of the dataset used to train the AMSS. Ministry of National Development (MND) National Research Foundation (NRF) Submitted/Accepted version This work was supported by the National Research Foundation, Singapore, and Ministry of National Development, Singapore under the Cities of Tomorrow R&D Program (CoT Award: COT-V4-2020-1). 2023-09-18T05:06:35Z 2023-09-18T05:06:35Z 2023 Conference Paper Lam, B., Ong, Z., Ooi, K., Ong, W., Wong, T., Gan, W. & Watcharasupat, K. N. (2023). Preliminary investigation of the short-term in situ performance of an automatic masker selection system. 52nd International Congress and Exposition on Noise Control Engineering (Inter-Noise 2023). https://hdl.handle.net/10356/168669 https://internoise2023.org/program/ en COT-V4-2020-1 © 2023 The Author(s). All rights reserved. This paper was published in the Proceedings of 52nd International Congress and Exposition on Noise Control Engineering (Inter-Noise 2023) and is made available with permission of The Author(s). application/pdf |
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Science::Physics::Acoustics Engineering::Electrical and electronic engineering Soundscape Soundscape Augmentation Auditory Masking Park Perceived Affectie Quality Lam, Bhan Ong, Zhen-Ting Ooi, Kenneth Ong, Wen-Hui Wong, Trevor Gan, Woon-Seng Watcharasupat, Karn N. Preliminary investigation of the short-term in situ performance of an automatic masker selection system |
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Soundscape augmentation or ``masking'' introduces wanted sounds into the acoustic environment to improve acoustic comfort. Usually, the masker selection and playback strategies are either arbitrary or based on simple rules (e.g. –3 dBA), which may lead to sub-optimal increment or even reduction in acoustic comfort for dynamic acoustic environments. To reduce ambiguity in the selection of maskers, an automatic masker selection system (AMSS) was recently developed. The AMSS uses a deep-learning model trained on a large-scale dataset of subjective responses to maximize the derived ISO pleasantness (ISO 12913-2). Hence, this study investigates the short-term in situ performance of the AMSS implemented in a gazebo in an urban park. Firstly, the predicted ISO pleasantness from the AMSS is evaluated in comparison to the in situ subjective evaluation scores. Secondly, the effect of various masker selection schemes on the perceived affective quality and appropriateness would be evaluated. In total, each participant evaluated 6 conditions: (1) ambient environment with no maskers; (2) AMSS; (3) bird and (4) water masker from prior art; (5) random selection from same pool of maskers used to train the AMSS; and (6) selection of best-performing maskers based on the analysis of the dataset used to train the AMSS. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Lam, Bhan Ong, Zhen-Ting Ooi, Kenneth Ong, Wen-Hui Wong, Trevor Gan, Woon-Seng Watcharasupat, Karn N. |
format |
Conference or Workshop Item |
author |
Lam, Bhan Ong, Zhen-Ting Ooi, Kenneth Ong, Wen-Hui Wong, Trevor Gan, Woon-Seng Watcharasupat, Karn N. |
author_sort |
Lam, Bhan |
title |
Preliminary investigation of the short-term in situ performance of an automatic masker selection system |
title_short |
Preliminary investigation of the short-term in situ performance of an automatic masker selection system |
title_full |
Preliminary investigation of the short-term in situ performance of an automatic masker selection system |
title_fullStr |
Preliminary investigation of the short-term in situ performance of an automatic masker selection system |
title_full_unstemmed |
Preliminary investigation of the short-term in situ performance of an automatic masker selection system |
title_sort |
preliminary investigation of the short-term in situ performance of an automatic masker selection system |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/168669 https://internoise2023.org/program/ |
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1779156317943889920 |