论文标题
用机器学习方法进行的几次生物声学事件检测
Few-shot Bioacoustic Event Detection with Machine Learning Methods
论文作者
论文摘要
几乎没有学习的类型是一种基于每个类别数量的样本进行预测的类型。这种类型的分类有时被称为元学习问题，其中模型学习如何学习识别罕见情况。我们寻求从哺乳动物或鸟类的五个示例性发声中提取信息，并在现场记录中检测和分类这些声音[2]。该任务是在2021年声学场景和事件（DCASE）挑战的检测和分类中提供的。我们并没有像最常见的那样使用深度学习，而是仅使用机器学习方法制定了一种新颖的解决方案。测试了各种模型，发现逻辑回归的表现优于线性回归和模板匹配。但是，所有这些方法都过度预测了现场记录中事件的数量。
Few-shot learning is a type of classification through which predictions are made based on a limited number of samples for each class. This type of classification is sometimes referred to as a meta-learning problem, in which the model learns how to learn to identify rare cases. We seek to extract information from five exemplar vocalisations of mammals or birds and detect and classify these sounds in field recordings [2]. This task was provided in the Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge of 2021. Rather than utilize deep learning, as is most commonly done, we formulated a novel solution using only machine learning methods. Various models were tested, and it was found that logistic regression outperformed both linear regression and template matching. However, all of these methods over-predicted the number of events in the field recordings.
