ARAUSv2: an expanded dataset and multimodal models of affective responses to augmented urban soundscapes

The ARAUS (Affective Responses to Augmented Urban Soundscapes) dataset consists of a five-fold cross-validation set and independent test set of subjective perceptual responses to augmented soundscapes presented as audio-visual stimuli. However, key limitations in its original release included a disp...

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Main Authors: Ooi, Kenneth, Ong, Zhen-Ting, Lam, Bhan, Wong, Trevor, Gan, Woon-Seng, Watcharasupat, Karn N.
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/168665
https://internoise2023.org/program/
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1686652023-09-22T15:39:04Z ARAUSv2: an expanded dataset and multimodal models of affective responses to augmented urban soundscapes Ooi, Kenneth Ong, Zhen-Ting Lam, Bhan 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) Science::Physics::Acoustics Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Soundscape Dataset Regression Deep Neural Network Soundscape Augmentation Auditory Masking The ARAUS (Affective Responses to Augmented Urban Soundscapes) dataset consists of a five-fold cross-validation set and independent test set of subjective perceptual responses to augmented soundscapes presented as audio-visual stimuli. However, key limitations in its original release included a disproportionate number of participants being young university students and a relatively small test set. We aim to address this by publishing ARAUSv2, which adds responses from 60 participants to the cross-validation from an older, non-student population, as well as responses from additional participants in a substantially larger test set consisting of new urban soundscapes recorded in a variety of settings in Singapore. The additional responses were collected in a similar fashion as the initial release, with participants rating augmented soundscapes (made by digitally adding maskers to urban soundscape recordings) on how pleasant, annoying, eventful, uneventful, vibrant, monotonous, chaotic, calm, and appropriate they were. We also present a sample of multimodal prediction models for the ISO Pleasantness and Eventfulness of the augmented soundscapes in ARAUSv2. The multimodal models use participant-linked information such as demographics and responses to psychological questionnaires, as well as visual information from the stimuli, which the baseline models presented in the initial ARAUS dataset did not utilize. 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). 2023-09-18T01:40:25Z 2023-09-18T01:40:25Z 2023 Conference Paper Ooi, K., Ong, Z., Lam, B., Wong, T., Gan, W. & Watcharasupat, K. N. (2023). ARAUSv2: an expanded dataset and multimodal models of affective responses to augmented urban soundscapes. 52nd International Congress and Exposition on Noise Control Engineering (Inter-Noise 2023). https://hdl.handle.net/10356/168665 https://internoise2023.org/program/ en COT-V4-2020-1 10.21979/N9/9OTEVX © 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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Physics::Acoustics
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Soundscape
Dataset
Regression
Deep Neural Network
Soundscape Augmentation
Auditory Masking
spellingShingle Science::Physics::Acoustics
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Soundscape
Dataset
Regression
Deep Neural Network
Soundscape Augmentation
Auditory Masking
Ooi, Kenneth
Ong, Zhen-Ting
Lam, Bhan
Wong, Trevor
Gan, Woon-Seng
Watcharasupat, Karn N.
ARAUSv2: an expanded dataset and multimodal models of affective responses to augmented urban soundscapes
description The ARAUS (Affective Responses to Augmented Urban Soundscapes) dataset consists of a five-fold cross-validation set and independent test set of subjective perceptual responses to augmented soundscapes presented as audio-visual stimuli. However, key limitations in its original release included a disproportionate number of participants being young university students and a relatively small test set. We aim to address this by publishing ARAUSv2, which adds responses from 60 participants to the cross-validation from an older, non-student population, as well as responses from additional participants in a substantially larger test set consisting of new urban soundscapes recorded in a variety of settings in Singapore. The additional responses were collected in a similar fashion as the initial release, with participants rating augmented soundscapes (made by digitally adding maskers to urban soundscape recordings) on how pleasant, annoying, eventful, uneventful, vibrant, monotonous, chaotic, calm, and appropriate they were. We also present a sample of multimodal prediction models for the ISO Pleasantness and Eventfulness of the augmented soundscapes in ARAUSv2. The multimodal models use participant-linked information such as demographics and responses to psychological questionnaires, as well as visual information from the stimuli, which the baseline models presented in the initial ARAUS dataset did not utilize.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Ooi, Kenneth
Ong, Zhen-Ting
Lam, Bhan
Wong, Trevor
Gan, Woon-Seng
Watcharasupat, Karn N.
format Conference or Workshop Item
author Ooi, Kenneth
Ong, Zhen-Ting
Lam, Bhan
Wong, Trevor
Gan, Woon-Seng
Watcharasupat, Karn N.
author_sort Ooi, Kenneth
title ARAUSv2: an expanded dataset and multimodal models of affective responses to augmented urban soundscapes
title_short ARAUSv2: an expanded dataset and multimodal models of affective responses to augmented urban soundscapes
title_full ARAUSv2: an expanded dataset and multimodal models of affective responses to augmented urban soundscapes
title_fullStr ARAUSv2: an expanded dataset and multimodal models of affective responses to augmented urban soundscapes
title_full_unstemmed ARAUSv2: an expanded dataset and multimodal models of affective responses to augmented urban soundscapes
title_sort arausv2: an expanded dataset and multimodal models of affective responses to augmented urban soundscapes
publishDate 2023
url https://hdl.handle.net/10356/168665
https://internoise2023.org/program/
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