Song recordings and annotation files of 3 canaries used to evaluate training of TweetyNet models for birdsong segmentation and annotation

Many analyses of birdsong require time-consuming manual annotation of the individual elements of song, known as syllables or notes. We developed the first automated algorithm for birdsong annotation, \"TweetyNet\", that is applicable to complex song such as canary song. TweetyNet is trained with a small amount of hand-labeled data using supervised learning methods. We evaluate the amount of data required for training TweetyNet models using vocalizations of two songbird species - Bengalese finches and Canaries. This dataset contains song audio files and their accompanying annotation files for the three canaries used in this analysis.鸟类鸣唱领域的诸多分析研究，均需对鸣曲的单个构成单元——即音节（syllables）与鸣音（notes）——开展耗时耗力的人工标注。本研究开发了首款面向金丝雀鸣曲等复杂鸣唱场景的鸟类鸣唱自动标注算法TweetyNet。该算法采用监督学习方法，仅需少量人工标注数据即可完成训练。本研究以孟加拉雀（Bengalese finches）与金丝雀（Canaries）两种鸣禽的鸣叫声为实验对象，评估了训练TweetyNet模型所需的数据量。本数据集包含本次分析所用的3只金丝雀的鸣曲音频文件及其配套标注文件。