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Title: Probably pleasant? A neural-probabilistic approach to automatic masker selection for urban soundscape augmentation
Authors: Ooi, Kenenth
Watcharasupat, Karn N.
Lam, Bhan
Ong, Zhen-Ting
Gan, Woon-Seng 
Keywords: Engineering::Electrical and electronic engineering
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Social sciences::Psychology::Applied psychology
Issue Date: 2022
Source: Ooi, K., Watcharasupat, K. N., Lam, B., Ong, Z. & Gan, W. (2022). Probably pleasant? A neural-probabilistic approach to automatic masker selection for urban soundscape augmentation. 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2022), 8887-8891.
Project: CoT-V4-2020-1 
Conference: 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2022)
Abstract: Soundscape augmentation, which involves the addition of sounds known as “maskers” to a given soundscape, is a human-centric urban noise mitigation measure aimed at improving the overall soundscape quality. However, the choice of maskers is often predicated on laborious processes and is inflexible to the time-varying nature of real-world soundscapes. Owing to the perceptual uniqueness of each soundscape and the inherent subjectiveness of human perception, we propose a probabilistic perceptual attribute predictor (PPAP) that predicts parameters of random distributions as outputs instead of a single deterministic value. Using the PPAP, we developed a novel automatic masker selection system (AMSS), which selects optimal masker candidates based on the predicted distribution of the ISO 12913-3 Pleasantness score for a given soundscape. Via a large-scale listening test with 300 participants, we collected 12600 subjective responses, each to a unique augmented soundscape, to train the PPAP models in a 5-fold cross-validation scheme. Using a convolutional recurrent neural network backbone and experimenting with several variants of the attention mechanism for the PPAP, we evaluated the proposed system on a blind test set with 48 unseen augmented soundscapes to assess the effectiveness of the probabilistic output scheme over traditional deterministic systems.
ISBN: 978-1-6654-0540-9
ISSN: 2379-190X
DOI: 10.1109/ICASSP43922.2022.9746897
DOI (Related Dataset): 10.21979/N9/YSJQKD
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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