This work introduces the one-shot learning paradigm in the computational bioacoustics domain. Even though, most of the related literature assumes availability of data characterizing the entire class dictionary of the problem at hand, that is rarely true as a habitat's species composition is only known up to a certain extent. Thus, the problem needs to be addressed by methodologies able to cope with non-stationarity. To this end, we propose a framework able to detect changes in the class dictionary and incorporate new classes on the fly. We design an one-shot learning architecture composed of a Siamese Neural Network operating in the logMel spectrogram space. We extensively examine the proposed approach on two datasets of various bird species using suitable figures of merit. Interestingly, such a learning scheme exhibits state of the art performance, while taking into account extreme non-stationarity cases.

中文翻译：

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一站式学习，可在非平稳环境中对鸟类进行声学识别

这项工作介绍了计算生物声学领域的一站式学习范例。即使大多数相关文献都假定有可用数据来描述当前问题的整个分类词典，但这很少是正确的，因为仅在一定程度上知道了栖息地的物种组成。因此，需要通过能够应对非平稳性的方法来解决该问题。为此，我们提出了一个框架，该框架能够检测类字典中的变化并动态添加新类。我们设计了一个由logMel频谱图空间中运行的暹罗神经网络组成的一次性学习体系结构。我们使用合适的品质因数在各种鸟类的两个数据集上广泛研究了提出的方法。有趣的是，