We analyse multi-purpose audio using tools to visualise similarities within the data that may be observed via unsupervised methods. The success of machine learning classifiers is affected by the information contained within system inputs, so we investigate whether latent patterns within the data may explain performance limitations of such classifiers. We use the visual assessment of cluster tendency (VAT) technique on a well known data set to observe how the samples naturally cluster, and we make comparisons to the labels used for audio geotagging and acoustic scene classification. We demonstrate that VAT helps to explain and corroborate confusions observed in prior work to classify this audio, yielding greater insight into the performance - and limitations - of supervised classification systems. While this exploratory analysis is conducted on data for which we know the "ground truth" labels, this method of visualising the natural groupings as dictated by the data leads to important questions about unlabelled data that can help the evaluation and realistic expectations of future (including self-supervised) classification systems.
Exploring bat song syllable representations in self-supervised audio encoders
Institute for Logic, Language and Computation, University of Amsterdam（阿姆斯特丹大学逻辑、语言与计算研究所）；Museum für Naturkunde, Leibniz Institute for Evolution and Biodiversity Science（莱布尼茨进化与生物多样性科学自然博物馆）
Exploring Text-Queried Sound Event Detection with Audio Source Separation
School of Marine Science and Technology, Northwestern Polytechnical University（西北工业大学海洋科学与技术学院）；Alibaba Group（阿里巴巴集团）；Fortemedia Singapore（Fortemedia新加坡公司）
本文提出 TQ-SED 框架，通过预训练改进的 AudioSep-DP 语言查询音频源分离模型分离重叠事件，再由多分支检测目标声音，显著提升 SED 性能。
Visually-Aware Audio Captioning With Adaptive Audio-Visual Attention
University of Surrey（萨里大学）；ByteDance（字节跳动）；Xi'an Jiaotong-Liverpool University（西交利物浦大学）；Southern University of Science and Technology（南方科技大学）；Izmir Katip Celebi University（伊兹密尔卡蒂普·切莱比大学）
Visually-Guided Sound Source Separation with Audio-Visual Predictive Coding
Xi’an Jiaotong University（西安交通大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）
Audio Description from Image by Modal Translation Network
Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences（中国科学院西安光学精密机械研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Northwestern Polytechnical University（西北工业大学）
BYOL for Audio: Exploring Pre-trained General-purpose Audio Representations
Communication Science Laboratories, Nippon Telegraph and Telephone Corporation（日本电报电话公司通信科学实验室）
预训练模型是现代机器学习系统中各个领域的特征提取器。在这项研究中，我们假设，表示有效的一般音频任务应该提供多个方面的强大功能的输入声音。为了识别声音而不管诸如变化的音高或音色的扰动，特征应该对这些扰动是鲁棒的。为了满足诸如情感或音乐类型识别等任务的不同需求，表征应该提供多方面的信息，例如局部和全局特征。为了实现我们的原则，我们提出了一种自监督学习方法：Bootstrap Your Own Latent（BYOL）for Audio（BYOL-A，发音为“viola”）。BYOL-A预训练对音频数据增强不变量的输入声音的表示，这使得学习的表示对声音的扰动具有鲁棒性。而BYOL-A编码器结合了局部和全局特征，并计算它们的统计信息，使表示提供多方面的信息。因此，学习的表示应该提供强大的和多方面的信息，以满足不同任务的各种需求。我们评估了BYOL-A与以前最先进的方法相比的一般音频任务性能，BYOL-A表现出了最佳平均结果72.4%和最佳VoxCeleb 1结果57.6%的泛化能力。大量的烧蚀实验表明，BYOL-A编码器架构有助于大部分性能，最后的关键部分诉诸BYOL框架和BYOL-A增强。我们的代码可以在https://github.com/nttcslab/byol-a上在线获得，以供将来研究。
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
Fraunhofer Heinrich-Hertz-Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（BIFOLD——柏林学习与数据基础研究所）
Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data
South China University of Technology（华南理工大学）；University of Georgia（佐治亚大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital（麻省总医院）
Audio-Visual Segmentation by Exploring Cross-Modal Mutual Semantics
University of Technology Sydney（悉尼科技大学）；The University of Queensland（昆士兰大学）；Futureverse；The Hong Kong University of Science and Technology（香港科技大学）；CSIRO DATA61（联邦科学与工业研究组织数据61研究所）；Netease Fuxi AI Lab（网易伏羲人工智能实验室）
Exploring Audio Cues for Enhanced Test-Time Video Model Adaptation
Artificial Intelligence Research Institute, Shenzhen MSU-BIT University and Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing（人工智能研究院、深圳MSU-BIT大学及粤港澳大湾区情感智能与普适计算联合实验室）；School of Software Engineering, South China University of Technology（华南理工大学软件学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
University of Cambridge（剑桥大学）；University of Southampton（南安普顿大学）
Exploring the User Experience of AI-Assisted Sound Searching Systems for Creative Workflows
Centre for Vision, Speech and Signal Processing, University of Surrey（视觉、语音和信号处理中心，萨里大学）；Research and Development, British Broadcasting Corporation（英国广播公司研发部）
Exploring the Potential of Data-Driven Spatial Audio Enhancement Using a Single-Channel Model
Faculdade de Engenharia Elétrica e de Computação（电气与计算工程学院）；Universidade Estadual de Campinas（坎皮纳斯州立大学）；Faculdade de Engenharia Acústica（声学工程学院）；Universidade Federal de Santa Maria（圣玛丽亚联邦大学）
