For centuries researchers have used sound to monitor and study wildlife. Traditionally, conservationists have identified species by ear; however, it is now common to deploy audio recording technology to monitor animal and ecosystem sounds. Animals use sound for communication, mating, navigation and territorial defence. Animal sounds provide valuable information and help conservationists to quantify biodiversity. Acoustic monitoring has grown in popularity due to the availability of diverse sensor types which include camera traps, portable acoustic sensors, passive acoustic sensors, and even smartphones. Passive acoustic sensors are easy to deploy and can be left running for long durations to provide insights on habitat and the sounds made by animals and illegal activity. While this technology brings enormous benefits, the amount of data that is generated makes processing a time-consuming process for conservationists. Consequently, there is interest among conservationists to automatically process acoustic data to help speed up biodiversity assessments. Processing these large data sources and extracting relevant sounds from background noise introduces significant challenges. In this paper we outline an approach for achieving this using state of the art in machine learning to automatically extract features from time-series audio signals and modelling deep learning models to classify different bird species based on the sounds they make. The acquired bird songs are processed using mel-frequency cepstrum (MFC) to extract features which are later classified using a multilayer perceptron (MLP). Our proposed method achieved promising results with 0.74 sensitivity, 0.92 specificity and an accuracy of 0.74.
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）
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）
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）
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）
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）
DeepForestSound: a multi-species automatic detector for passive acoustic monitoring in African tropical forests, a case study in Kibale National Park
N'lab, Nitidae Association, Montpellier, France（N'lab，Nitidae协会，蒙彼利埃，法国）；Institut de Systématique, Evolution, Biodiversité (ISYEB), UMR7205 Centre National de la Recherche Scientifique, MNHN, SU, EPHE-PSL, UA, Paris, France（系统学、进化与生物多样性研究所（ISYEB），法国国家科学研究中心联合实验室7205，法国国家自然历史博物馆，索邦大学，巴黎高等研究实践学院-巴黎文理研究大学，巴黎，法国）
School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University（康奈尔大学鸟类学实验室K. Lisa Yang保护生物声学中心）
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）
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）
SEABAD: A Tropical Bird Activity Detection Dataset for Passive Acoustic Monitoring
Faculty of Computer Science and Information Technology, Universiti Malaya（马来亚大学计算机科学与信息技术学院）；Faculty of Electrical Engineering, Universiti Teknologi Malaysia（马来西亚科技大学电气工程学院）
TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors
Freie Universität Berlin（柏林自由大学）；Berlin University of Applied Sciences（柏林应用科学大学）；Woks Audio GmbH（沃克斯音频有限公司）；Royal Holloway University of London（伦敦大学皇家霍洛威学院）；Einstein Center for Digital Future（数字未来爱因斯坦中心）；Inria（法国国家信息与自动化研究所）
A noise-robust acoustic method for recognizing foraging activities of grazing cattle
Mid Sweden University（中瑞典大学）；Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional（信号、系统与计算智能研究所）；CONICET（阿根廷科学与技术研究委员会）；TERRA Teaching and Research Center（TERRA教学研究中心）；University of Liège（列日大学）；Gembloux Agro-Bio Tech（让布卢农业生物技术学院）；Universidad Nacional de Rosario（罗萨里奥国立大学）；Instituto de Investigaciones en Ciencias Agrarias de Rosario（罗萨里奥农业科学研究所）；Michigan State University（密歇根州立大学）；New Mexico State University（新墨西哥州立大学）
A strongly annotated passive acoustic dataset for tropical bird monitoring
Microsoft AI for Good Research Lab（微软AI for Good研究实验室）；Instituto de Investigación de Recursos Biológicos Alexander von Humboldt（亚历山大冯·洪堡生物资源研究所）；Center for Research and Formation in Artificial Intelligence（人工智能研究与培养中心）；Fundación Manacus（曼卡斯基金会）；Louisiana State University（路易斯安那州立大学）；Museum of Natural Sciences（自然博物馆）
Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Imperial College London（帝国理工学院）；IDSIA, University of Lugano（卢加诺大学IDSIA研究所）；Twitter（Twitter公司）；Haifa University（海法大学）；National Geographic Society（国家地理学会）；Dominica Sperm Whale Project（多米尼加抹香鲸项目）；Carleton University（卡尔顿大学）；Simons Institute for the Theory of Computing, University of California, Berkeley（加利福尼亚大学伯克利分校西蒙斯计算理论研究所）；Baruch College（巴鲁克学院）；Google Research（谷歌研究院）；Ocean Alliance（海洋联盟）；ISI Foundation（ISI基金会）；Aarhus University（奥胡斯大学）；Harvard University（哈佛大学）
Full-Stack Bioacoustics: Field Kit to AI to Action (Workshop report)
Naturalis Biodiversity Center（自然生物多样性中心）；Tilburg University（蒂尔堡大学）；Universiteit van Amsterdam（阿姆斯特丹大学）；CODE University of Applied Sciences Berlin（柏林CODE应用科学大学）；University of Cambridge（剑桥大学）
ForestIR: Physics-Informed Forest Sound Simulation for Array-Based Bioacoustic Remote Sensing
Department of Statistical Science, Duke University（统计科学系，杜克大学）；Department of Biological and Environmental Science, University of Jyväskylä（生物与环境科学系，于韦斯屈耶大学）；Faculty of Biological and Environmental Sciences, University of Helsinki（生物与环境科学学院，赫尔辛基大学）；Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki（生物与进化生物学研究计划，生物与环境科学学院，赫尔辛基大学）；Digital Services, University of Jyväskylä（数字服务，于韦斯屈耶大学）；Open Science Centre, University of Jyväskylä（开放科学中心，于韦斯屈耶大学）
