论文标题
冲浪板：现代机器学习的音频功能提取
Surfboard: Audio Feature Extraction for Modern Machine Learning
论文作者
论文摘要
我们介绍了Surfboard，这是一个开源python库，用于提取具有应用于医疗域的音频功能。冲浪板的书面目的是解决现有库的痛点，并促进与现代机器学习框架的联合用途。可以在Python和命令行接口中以编程方式访问该软件包，从而可以轻松地集成在机器学习工作流程中。它以最新的音频分析软件包为基础，并为处理大型工作负载提供多处理支持。我们回顾了类似的框架并描述冲浪板的架构，包括其功能的临床动机。使用MPower数据集，我们说明了Surfboard在帕金森氏病分类任务中的应用，并强调了现有研究中的常见陷阱。源代码已向研究界开放，以促进临床领域的未来音频研究。
We introduce Surfboard, an open-source Python library for extracting audio features with application to the medical domain. Surfboard is written with the aim of addressing pain points of existing libraries and facilitating joint use with modern machine learning frameworks. The package can be accessed both programmatically in Python and via its command line interface, allowing it to be easily integrated within machine learning workflows. It builds on state-of-the-art audio analysis packages and offers multiprocessing support for processing large workloads. We review similar frameworks and describe Surfboard's architecture, including the clinical motivation for its features. Using the mPower dataset, we illustrate Surfboard's application to a Parkinson's disease classification task, highlighting common pitfalls in existing research. The source code is opened up to the research community to facilitate future audio research in the clinical domain.
