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 sound-scape augmentation.
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation Learning
Ghent University（根特大学）；University of Leicester（莱斯特大学）；Shenzhen University（深圳大学）；University College London（伦敦大学学院）；Ghent University-Imec（根特大学-imec）；University of Surrey（萨里大学）
Soundscape Captioning using Sound Affective Quality Network and Large Language Model
Machine Learning Group, University of Oxford（牛津大学机器学习组）；AIRO-IDLab, Ghent University-Imec（根特大学-imec AIRO-IDLab）；CVSSP, University of Surrey（萨里大学计算机视觉与模式识别中心）；University College London（伦敦大学学院）；WAVES Group, Ghent University（根特大学WAVES组）
Automating Urban Soundscape Enhancements with AI: In-situ Assessment of Quality and Restorativeness in Traffic-Exposed Residential Areas
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Center for Music Technology, Georgia Institute of Technology（佐治亚理工学院音乐技术中心）；Building & Research Institute, Housing & Development Board（建屋发展局建筑与研究院）；Department of Architectural Engineering, Chungnam National University（忠南大学建筑工程系）；UCL Institute for Environmental Design and Engineering, The Bartlett, University College London（伦敦大学学院巴特lett环境设计与工程学院）；Nanyang Business School, Nanyang Technological University（南洋理工大学南洋商学院）
Generating Diverse Audio-Visual 360 Soundscapes for Sound Event Localization and Detection
University of Southern California（南加州大学）；Universidad Nacional Autónoma de México（墨西哥国立自治大学）；Universidad Autónoma de Tamaulipas（塔毛利帕斯自治大学）；Queen Mary University of London（伦敦大学玛丽皇后学院）
The impact of non-target events in synthetic soundscapes for sound event detection
Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Loria（洛林计算机科学实验室）；Department of Information Engineering, Universita Politecnica delle Marche（马尔凯理工大学信息工程系）
Misophonia Trigger Sound Detection on Synthetic Soundscapes Using a Hybrid Model with a Frozen Pre-Trained CNN and a Time-Series Module
Department of Computer Science, Nagoya Institute of Technology（名古屋技术大学计算机科学系）；International Research Center for Neurointelligence, The University of Tokyo（东京大学神经智能国际研究中心）
Spatial Scaper: A Library to Simulate and Augment Soundscapes for Sound Event Localization and Detection in Realistic Rooms
Music and Audio Research Laboratory, New York University（纽约大学音乐与音频研究实验室）；Viterbi School of Engineering, University of Southern California（南加州大学维特比工程学院）
