Acoustic identification of individual animals with hierarchical contrastive learning
Centre for Digital Music, Queen Mary University of London（伦敦大学玛丽皇后学院数字音乐中心）；IMT Atlantique（IMT大西洋高等电信学院）；CNRS（法国国家科学研究中心）；Lab-STICC（信息、信号与智能系统实验室）；Tilburg University（蒂尔堡大学）
Ines Nolasco, Ilyass Moummad, Dan Stowell, Emmanouil Benetos
Acoustic identification of individual animals (AIID) is closely related to audio-based species classification but requires a finer level of detail to distinguish between individual animals within the same species. In this work, we frame AIID as a hierarchical multi-label classification task and propose the use of hierarchy-aware loss functions to learn robust representations of individual identities that maintain the hierarchical relationships among species and taxa. Our results demonstrate that hierarchical embeddings not only enhance identification accuracy at the individual level but also at higher taxonomic levels, effectively preserving the hierarchical structure in the learned representations. By comparing our approach with non-hierarchical models, we highlight the advantage of enforcing this structure in the embedding space. Additionally, we extend the evaluation to the classification of novel individual classes, demonstrating the potential of our method in open-set classification scenarios.
A Multi-view CNN-based Acoustic Classification System for Automatic Animal Species Identification
Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；Department of Computer and Information Sciences, Northumbria University（诺森比亚大学计算机与信息科学系）
Air Traffic Management Research Institute, Nanyang Technological University, Singapore（新加坡南洋理工大学航空交通管理研究所）；Center of AI Research, VinUniversity, Vietnam（越南Vin大学人工智能研究中心）
Acoustic evaluation of a neural network dedicated to the detection of animal vocalisations
University of Saint-Etienne（圣埃蒂安大学）；BioPhonia；Muséum National d'Histoire Naturelle（法国国家自然历史博物馆）；Sorbonne Université（索邦大学）；EPHE（法国高等研究实践学院）；Université des Antilles（安的列斯大学）；Institut d’Optique Graduate School（法国光学研究生院）；Inria（法国国家信息与自动化研究所）；Office Français de la Biodiversité（法国生物多样性局）
Automatic acoustic identification of individual animals: Improving generalisation across species and recording conditions
Queen Mary University of London（伦敦玛丽女王大学）；Charles University（查理大学）；The Czech Academy of Sciences（捷克科学院）；Czech University of Life Sciences Prague（布拉格捷克生命科学大学）；Adam Mickiewicz University（亚当·密茨凯维奇大学）
School of Computing and Information Systems, The University of Melbourne, Australia（墨尔本大学计算机与信息系统学院）；Baskin School of Engineering, University of California, Santa Cruz, USA（加州大学圣克鲁兹分校工程学院）；Institute of Trustworthy Embodied AI, Fudan University, China（复旦大学可信具身人工智能研究所）
BioSEN: A Bio-acoustic Signal Enhancement Network for Animal Vocalizations
Graduate School of Bioresource and Bioenvironmental Science, Kyushu University（九州大学生物资源与生物环境科学研究生院）；Faculty of Agriculture, Kyushu University（九州大学农学部）；School of Information and Physical Sciences, University of Newcastle（新castle大学信息与物理科学学院）；International Institute for Carbon-Neutral Energy Research, Kyushu University（九州大学国际碳中性能源研究所）
A Machine Learning Enhanced Approach for Automated Sunquake Detection in Acoustic Emission Maps
Technical University of Cluj-Napoca（克卢日-纳波卡技术大学）；National Center for Atmospheric Research（美国国家大气研究中心）；Astronomical Institute of the Romanian Academy（罗马尼亚科学院天文研究所）
Chimpanzee voice prints? Insights from transfer learning experiments from human voices
University of Zurich（苏黎世大学）；Budongo Conservation Field Station（布东戈保护野外研究站）；Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich（苏黎世大学语言进化跨学科研究中心）；University of Warwick（华威大学）
SMSAT: A Multimodal Acoustic Dataset and Deep Contrastive Learning Framework for Affective and Physiological Modeling of Spiritual Meditation
AITeC, National Center for Physics (NCP), Pakistan（AITeC，国家物理研究中心（NCP），巴基斯坦）；Imam Mohammad Ibn Saud Islamic University (IMSIU)（伊玛目穆罕默德·伊本·沙特伊斯兰大学）；European Organization for Nuclear Research, CERN, Switzerland（欧洲核子研究中心，CERN，瑞士）；University of Engineering (UET), Taxila（工程大学（UET），塔西拉）；Department of Computer Engineering and the Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals (KFUPM)（计算机工程系及智能物流与移动性交叉研究中心，国王法赫德石油与矿物大学（KFUPM））；College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing（电信与信息工程学院，南京邮电大学，南京）
ARIONet: An Advanced Self-supervised Contrastive Representation Network for Birdsong Classification and Future Frame Prediction
Department of Computer Science and Engineering, United International University（计算机科学与工程系，国际大学）；Faculty of Science and Technology, Charles Darwin University（科学与技术学院，查尔斯达尔文大学）；Faculty of Science and Information Technology, Charles Darwin University（科学与信息技术学院，查尔斯达尔文大学）
CoSTALA: Compositional Spatio-Temporal Audio-Language Alignment via Multi-Grain Hierarchical Contrastive Learning
Xi’an Jiaotong Liverpool University（西交利物浦大学）；MiLM Plus, Xiaomi Inc.（小米公司MiLM Plus）；University of Oulu（奥卢大学）；Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）
2-Dimensional spectral gating for denoising bioacoustics recordings
Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Pioneer Centre for AI, University of Copenhagen（哥本哈根大学人工智能先锋中心）；McGill University（麦吉尔大学）；Stockholm University（斯德哥尔摩大学）
CoughPhase-CLR: Designing an acoustics-informed foundation model for coughing sound classification
Chair of Health Informatics at the TUM University Hospital（慕尼黑工业大学大学医院健康信息学教席）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Munich Data Science Institute（慕尼黑数据科学研究所）；University Hospital Augsburg at the University of Augsburg（奥格斯堡大学奥格斯堡大学医院）；Medical Faculty, Ludwig Maximilians University of Munich（慕尼黑路德维希-马克西米利安大学医学院）；Group on Language, Audio, & Music at Imperial College London（帝国理工学院语言、音频与音乐组）；Technical University of Munich（慕尼黑工业大学）
