Results of the clustering procedure.

The study of non-human animals’ communication systems generally relies on the transcription of vocal sequences using a finite set of discrete units. This set is referred to as a vocal repertoire, which is specific to a species or a sub-group of a species. When conducted by human experts, the formal description of vocal repertoires can be laborious and/or biased. This motivates computerised assistance for this procedure, for which machine learning algorithms represent a good opportunity. Unsupervised clustering algorithms are suited for grouping close points together, provided a relevant representation. This paper therefore studies a new method for encoding vocalisations, allowing for automatic clustering to alleviate vocal repertoire characterisation. Borrowing from deep representation learning, we use a convolutional auto-encoder network to learn an abstract representation of vocalisations. We report on the quality of the learnt representation, as well as of state of the art methods, by quantifying their agreement with expert labelled vocalisation types from 8 datasets of other studies across 6 species (birds and marine mammals). With this benchmark, we demonstrate that using auto-encoders improves the relevance of vocalisation representation which serves repertoire characterisation using a very limited number of settings. We also publish a Python package for the bioacoustic community to train their own vocalisation auto-encoders or use a pretrained encoder to browse vocal repertoires and ease unit wise annotation.

对非人类动物通讯系统的研究，通常依赖于使用有限离散单元集合对发声序列进行转录。该集合被称为发声曲目库（vocal repertoire），其针对特定物种或物种的亚群定制。若由人类专家开展此类工作，对发声曲目库的正式描述往往耗时费力且可能引入偏差。这便推动了该流程的计算机辅助方案，其中机器学习算法正是极具潜力的解决方案。无监督聚类算法可将相似样本归为一类，前提是拥有合理的特征表示。因此，本文研究一种全新的发声编码方法，支持通过自动聚类来简化发声曲目库的表征工作。借鉴深度表征学习的研究思路，我们采用卷积自编码器（convolutional auto-encoder）网络来学习发声信号的抽象特征表示。我们通过量化所学表示以及最先进方法的结果，与来自6个物种（鸟类与海洋哺乳动物）的8项其他研究数据集的专家标注发声类型之间的一致性，来评估二者的质量。借助该基准测试，我们证明：在仅需极少量配置参数的情况下，使用自编码器可提升发声表示的相关性，从而助力发声曲目库的表征工作。我们还开源了一个Python包，供生物声学界的研究者训练专属的发声自编码器，或使用预训练编码器来浏览发声曲目库、简化单元级标注流程。