Open audio databases such as Xeno-Canto are widely used to build datasets to explore bird song repertoire or to train models for automatic bird sound classification by deep learning algorithms. However, such databases suffer from the fact that bird sounds are weakly labelled: a species name is attributed to each audio recording without timestamps that provide the temporal localization of the bird song of interest. Manual annotations can solve this issue, but they are time consuming, expert-dependent, and cannot run on large datasets. Another solution consists in using a labelling function that automatically segments audio recordings before assigning a label to each segmented audio sample. Although labelling functions were introduced to expedite strong label assignment, their classification performance remains mostly unknown. To address this issue and reduce label noise (wrong label assignment) in large bird song datasets, we introduce a data-centric novel labelling function composed of three successive steps: 1) time-frequency sound unit segmentation, 2) feature computation for each sound unit, and 3) classification of each sound unit as bird song or noise with either an unsupervised DBSCAN algorithm or the supervised BirdNET neural network. The labelling function was optimized, validated, and tested on the songs of 44 West-Palearctic common bird species. We first showed that the segmentation of bird songs alone aggregated from 10% to 83% of label noise depending on the species. We also demonstrated that our labelling function was able to significantly reduce the initial label noise present in the dataset by up to a factor of three. Finally, we discuss different opportunities to design suitable labelling functions to build high-quality animal vocalizations with minimum expert annotation effort.
NIPS4Bplus: a richly annotated birdsong audio dataset
Machine Listening Lab, Centre for Digital Music (C4DM), Queen Mary Univ. of London, UK Centre d'Ecologie et des Sciences de la Conservation (CESCO), Mus\' e um National d'Histoire Naturelle, CNRS, Sorbonne Univ., Paris, France Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), CNRS, Univ. de Montpellier, Univ. Paul-Val\' e ry Montpellier, France AGH Univ. of Science；Technology, Department of Mechanics；Vibroacoustics, Krak\' o w, Poland Univ. Toulon, Aix Marseille Univ., CNRS, LIS, DYNI team, SABIOD, Marseille, France
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）
DB3V: A Dialect Dominated Dataset of Bird Vocalisation for Cross-corpus Bird Species Recognition
University of Augsburg（奥格斯堡大学）；Beijing Forestry University（北京林业大学）；Technical University of Munich（慕尼黑工业大学）；Hume AI（休谟人工智能公司）；Imperial College London（伦敦帝国理工学院）；Munich Data Science Institute（慕尼黑数据科学研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）
MuSFA: Improving Music Structural Function Analysis with Partially Labeled Data
SAMI, ByteDance（字节跳动SAMI）
音乐结构分析（MSA）系统旨在将歌曲记录分割成具有有用标签的非重叠部分。以前的MSA系统通常在后处理步骤中预测抽象标签，并需要歌曲的完整上下文。相比之下，我们最近提出了一个被称为“音乐结构功能分析”（MuSFA）的监督框架，该框架直接从音频中建模和预测有意义的标签，如“诗句”和“合唱”，而不需要歌曲的完整上下文。然而，该系统的性能取决于训练数据的数量和质量。在本文中，我们建议重新利用公共数据集HookTheory Lead Sheet Dataset（HLSD）来提高性能。HLSD包含超过18K的音乐片段摘录，最初收集用于研究自动旋律协调。我们将每个摘录视为部分标记的歌曲并提供标签映射，以便HLSD可以与其他公共数据集（如SALAMI，RWC和Isophonics）一起使用。在交叉数据集评估中，我们发现在训练中包含HLSD可以将最先进的边界检测和切片标记分数分别提高约3%和约1%。
JaCappella Corpus: A Japanese a Cappella Vocal Ensemble Corpus
Graduate School of Information Science and Technology, The University of Tokyo（东京大学信息科学与技术研究生院）；National Institute of Advanced Industrial Science and Technology (AIST)（国立研究开发法人产业技术综合研究所（AIST））
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）
A Bird Song Detector for improving bird identification through Deep Learning: a case study from Doñana
University of Córdoba（科尔多瓦大学）；Estación Biológica de Doñana（多纳纳生物站）；University of Cádiz（卡迪斯大学）；Universidad Politécnica de Madrid（马德里理工大学）；University of Sevilla（塞维利亚大学）
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）
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）
AudioProtoPNet: An interpretable deep learning model for bird sound classification
Fraunhofer Institute for Energy Economics and Energy System Technology (IEE)（弗劳恩霍夫能源经济与能源系统技术研究所）；Intelligent Embedded Systems (IES), University of Kassel（卡塞尔大学智能嵌入式系统研究所）
YuE2: Unifying Symbolic and Audio Music Generation at Frontier Quality
HKUST（香港科技大学）；M-A-P（M-A-P（多模态艺术投影））；New York University（纽约大学）；Stanford University（斯坦福大学）；MBZUAI（穆罕默德·本·扎耶德人工智能大学）；ACE Studio（ACE工作室）；HKGAI（香港生成式人工智能研究院）
