results: 这篇论文提供了一个 cetacean 叫声检测的基准数据集，并使用了这个基准数据集来评估深度学习算法的性能。Abstract
This paper presents Soundbay, an open-source Python framework that allows bio-acoustics and machine learning researchers to implement and utilize deep learning-based algorithms for acoustic audio analysis. Soundbay provides an easy and intuitive platform for applying existing models on one's data or creating new models effortlessly. One of the main advantages of the framework is the capability to compare baselines on different benchmarks, a crucial part of emerging research and development related to the usage of deep-learning algorithms for animal call analysis. We demonstrate this by providing a benchmark for cetacean call detection on multiple datasets. The framework is publicly accessible via https://github.com/deep-voice/soundbay
摘要
这篇论文介绍了Soundbay，一个开源Python框架，它使bio-acoustics和机器学习研究人员可以使用深度学习算法进行声音音频分析。Soundbay提供了一个简单易用的平台，使得研究人员可以轻松地应用现有的模型或创建新的模型。这个框架的一个主要优点是可以比较基线在不同的benchmark上，这是机器学习算法用于动物叫声分析领域的新研究和开发的关键部分。我们通过提供多个数据集上的 cetacean 叫声检测基准来说明这一点。框架可以通过https://github.com/deep-voice/soundbay访问。