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AI-based soundscape analysis: Jointly identifying sound sources and predicting annoyancea)

Hou, Yuanbo; Ren, Qiaoqiao; Zhang, Huizhong; Mitchell, Andrew; Aletta, Francesco; Kang, Jian; Botteldooren, Dick; (2023) AI-based soundscape analysis: Jointly identifying sound sources and predicting annoyancea). The Journal of the Acoustical Society of America , 154 (5) pp. 3145-3157. 10.1121/10.0022408. Green open access

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Abstract

Soundscape studies typically attempt to capture the perception and understanding of sonic environments by surveying users. However, for long-term monitoring or assessing interventions, sound-signal-based approaches are required. To this end, most previous research focused on psycho-acoustic quantities or automatic sound recognition. Few attempts were made to include appraisal (e.g., in circumplex frameworks). This paper proposes an artificial intelligence (AI)-based dual-branch convolutional neural network with cross-attention-based fusion (DCNN-CaF) to analyze automatic soundscape characterization, including sound recognition and appraisal. Using the DeLTA dataset containing human-annotated sound source labels and perceived annoyance, the DCNN-CaF is proposed to perform sound source classification (SSC) and human-perceived annoyance rating prediction (ARP). Experimental findings indicate that (1) the proposed DCNN-CaF using loudness and Mel features outperforms the DCNN-CaF using only one of them. (2) The proposed DCNN-CaF with cross-attention fusion outperforms other typical AI-based models and soundscape-related traditional machine learning methods on the SSC and ARP tasks. (3) Correlation analysis reveals that the relationship between sound sources and annoyance is similar for humans and the proposed AI-based DCNN-CaF model. (4) Generalization tests show that the proposed model's ARP in the presence of model-unknown sound sources is consistent with expert expectations and can explain previous findings from the literature on soundscape augmentation.

Type: Article
Title: AI-based soundscape analysis: Jointly identifying sound sources and predicting annoyancea)
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1121/10.0022408
Publisher version: https://doi.org/10.1121/10.0022408
Language: English
Additional information: © 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Audiometry, Auditory recognition, Spectrograms, Acoustic ecology, Transformer, Convolutional neural network, Deep learning, Artificial intelligence, Birds, Regression analysis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10181974
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