无需标注数据，自动拆分鸟鸣为音节序列并识别个体差异。
Identifying birdsong syllables without labelled data
- 基于事件检测、聚类和匹配追踪的无监督音节分解方法。
- 在雀鸟和大山雀数据集上性能接近人工标注，准确区分个体特征。
- 适合动物行为学、生态监测领域，无需人工标注即可分析鸟类发声。
识别鸟鸣中音节序列是解决鸟类个体识别、理解动物交流与感知运动学习等关键问题的基础。近年来，机器学习方法虽减轻了人工标注负担，但仍依赖标注数据，限制了其在少数物种和数据集上的应用。本文首次提出完全无监督的鸟鸣音节分解算法：先检测音节事件，再通过聚类提取音节模板，最后利用匹配追踪将录音分解为音节序列。在麻雀鸟鸣数据集上，该方法与人工标注相比表现优异；同时验证了其可区分同一物种内不同个体的独特声学特征，涵盖麻雀和大山雀两种物种。
原文摘要 · Abstract (English)
Identifying sequences of syllables within birdsongs is key to tackling a wide array of challenges, including bird individual identification and better understanding of animal communication and sensory-motor learning. Recently, machine learning approaches have demonstrated great potential to alleviate the need for experts to label long audio recordings by hand. However, they still typically rely on the availability of labelled data for model training, restricting applicability to a few species and datasets. In this work, we build the first fully unsupervised algorithm to decompose birdsong recordings into sequences of syllables. We first detect syllable events, then cluster them to extract templates -- syllable representations -- before performing matching pursuit to decompose the recording as a sequence of syllables. We evaluate our automatic annotations against human labels on a dataset of Bengalese finch songs and find that our unsupervised method achieves high performance. We also demonstrate that our approach can distinguish individual birds within a species through their unique vocal signatures, for both Bengalese finches and another species, the great tit.
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