This report presents deep learning and data augmentation techniques used by a system entered into the Few-Shot Bioacoustic Event Detection for the DCASE2021 Challenge. The remit was to develop a few-shot learning system for animal (mammal and bird) vocalisations. Participants were tasked with developing a method that can extract information from five exemplar vocalisations, or shots, of mammals or birds and detect and classify sounds in field recordings. In the system described in this report, prototypical networks are used to learn a metric space, from which classification is performed by computing the distance of a query point to class prototypes, classifying based on shortest distance. We describe the architecture of this network, feature extraction methods, and data augmentation performed on the given dataset and compare our work to the challenge's baseline networks.
Adaptive Learning via a Negative Selection Strategy for Few-Shot Bioacoustic Event Detection
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；Sanya Science and Education Innovation Park, Wuhan University of Technology（武汉理工大学三亚科教创新园）
Few-shot bioacoustic event detection at the DCASE 2022 challenge
Queen Mary University of London（伦敦玛丽女王大学）；Nantes Université（南特大学）；École Centrale Nantes（南特中央理工学院）；CNRS（法国国家科学研究中心）；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）；BIOTOPIA Naturkundemuseum Bayern（巴伐利亚BIOTOPIA自然历史博物馆）；AGH University of Science and Technology（AGH科技大学）；Cornell University（康奈尔大学）；Tilburg University（蒂尔堡大学）；Naturalis Biodiversity Centre（Naturalis生物多样性中心）；University Ramon Llull（拉蒙·柳利大学）；Oxford University（牛津大学）；Syracuse University（雪城大学）；University of Salford（索尔福德大学）；University of Surrey（萨里大学）；Sonantic Limited（Sonantic有限公司）
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）
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）
