The automatic classification of animal sounds presents an enduring challenge in bioacoustics, owing to the diverse statistical properties of sound signals, variations in recording equipment, and prevalent low Signal-to-Noise Ratio (SNR) conditions. Deep learning models like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have excelled in human speech recognition but have not been effectively tailored to the intricate nature of animal sounds, which exhibit substantial diversity even within the same domain. We propose an automated classification framework applicable to general animal sound classification. Our approach first optimizes audio features from Mel-frequency cepstral coefficients (MFCC) including feature rearrangement and feature reduction. It then uses the optimized features for the deep learning model, i.e., an attention-based Bidirectional LSTM (Bi-LSTM), to extract deep semantic features for sound classification. We also contribute an animal sound benchmark dataset encompassing oceanic animals and birds1. Extensive experimentation with real-world datasets demonstrates that our approach consistently outperforms baseline methods by over 25% in precision, recall, and accuracy, promising advancements in animal sound classification.
Robust sound event detection in bioacoustic sensor networks
Cornell Lab of Ornithology, Cornell University（康奈尔大学康奈尔鸟类学实验室）；Music and Audio Research Laboratory, New York University（纽约大学音乐与音频研究实验室）；Center for Urban Science and Progress, New York University（纽约大学城市科学与进步中心）
Deep Attention-based Representation Learning for Heart Sound Classification
University of Augsburg（奥格斯堡大学）；The University of Tokyo（东京大学）；Shenzhen University General Hospital（深圳大学总医院）；Wenzhou Medical University First Affiliated Hospital（温州医科大学附属第一医院）；Imperial College London（帝国理工学院）
Learning to detect an animal sound from five examples
Queen Mary University of London（伦敦玛丽女王大学）；Nantes Universite, Ecole Centrale Nantes, CNRS, LS2N, UMR 6004（南特大学，南特中央理工大学，法国国家科学研究中心，LS2N，UMR 6004）；University of Konstanz（康斯坦茨大学）；Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所）；Universitat Ramon Llull（拉蒙·尤以大学）；BIOTOPIA Naturkundemuseum Bayern（巴伐利亚州自然博物馆BIOTOPIA）；AGH University of Science and Technology（AGH科技大学）；University of Salford（索尔福德大学）；University of Surrey（萨里大学）；Aarhus University（奥胡斯大学）；Syracuse University（雪城大学）；Woods Hole Oceanographic Institution（伍兹霍尔海洋研究所）
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）
Automatic Identification of Animal Breeds and Species Using Bioacoustics and Artificial Neural Networks
Research Collaboratory for Advanced Intelligent Systems（高级智能系统研究合作中心）；Institute of Computer Science, University of the Philippines Los Baños（菲律宾大学洛斯巴诺斯分校计算机科学学院）
Generalization in birdsong classification: impact of transfer learning methods and dataset characteristics
Naturalis Biodiversity Center（自然生物多样性中心）；Xeno-Canto Foundation（异音基金会）；Landesbund fur Vogel- und Naturschutz（鸟类与自然保护州联合会）；Department of Cognitive Science and Artificial Intelligence, Tilburg University（蒂尔堡大学认知科学与人工智能系）
Aligning Sight and Sound: Advanced Sound Source Localization Through Audio-Visual Alignment
School of Electrical Engineering, KAIST（韩国科学技术院电气工程学院）；Department of Electrical Engineering, POSTECH（浦项工科大学电气工程系）；School of Engineering and Applied Sciences at Harvard University（哈佛大学工程与应用科学学院）
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）
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）
AFT: An Exemplar-Free Class Incremental Learning Method for Environmental Sound Classification
South China University of Technology, China（南方科技大学）；The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳））；The University of Melbourne, Australia（墨尔本大学）
Detection and Classification of Cetacean Echolocation Clicks using Image-based Object Detection Methods applied to Advanced Wavelet-based Transformations
