使用监督对比学习预训练生物声学少样本检测的表示
Pretraining Representations for Bioacoustic Few-shot Detection using Supervised Contrastive Learning
AI总结：
本文利用监督对比学习与数据增强，从零预训练特征提取器，实现生物声学少样本事件检测，在DCASE挑战中取得第二名的成绩。
摘要：
深度学习近年来被广泛应用于声音事件检测与分类。其成功与足够大规模数据集的可用性密切相关，在监督学习场景下，可能还需要相应的标注。在生物声学应用中，大多数任务仅有少量标注训练数据，因为对长时间录音进行标注既耗时又昂贵。因此，监督学习并非解决生物声学任务的最佳方法。生物声学社区将声音事件检测问题重新定义为少样本学习框架，即仅使用少量标注示例来训练系统。DCASE挑战中的少样本生物声学声音事件检测任务，旨在仅针对每类目标类别提供五个标注示例的情况下，检测长时间录音中的事件。本文表明，通过利用监督对比学习框架结合数据增强，可以从零开始学习一个丰富的特征提取器。我们强调了该框架在训练数据中未见过的类别上进行五样本事件检测时的良好迁移能力。我们在验证集上获得了63.46%的F分数，在测试集上获得了42.7%的F分数，在DCASE挑战中排名第二。我们针对数据增强技术的关键选择以及应用于训练集的学习策略进行了消融研究。
英文摘要：
Deep learning has been widely used recently for sound event detection and classification. Its success is linked to the availability of sufficiently large datasets, possibly with corresponding annotations when supervised learning is considered. In bioacoustic applications, most tasks come with few labelled training data, because annotating long recordings is time consuming and costly. Therefore supervised learning is not the best suited approach to solve bioacoustic tasks. The bioacoustic community recasted the problem of sound event detection within the framework of few-shot learning, i.e. training a system with only few labeled examples. The few-shot bioacoustic sound event detection task in the DCASE challenge focuses on detecting events in long audio recordings given only five annotated examples for each class of interest. In this paper, we show that learning a rich feature extractor from scratch can be achieved by leveraging data augmentation using a supervised contrastive learning framework. We highlight the ability of this framework to transfer well for five-shot event detection on previously unseen classes in the training data. We obtain an F-score of 63.46\% on the validation set and 42.7\% on the test set, ranking second in the DCASE challenge. We provide an ablation study for the critical choices of data augmentation techniques as well as for the learning strategy applied on the training set.
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