ISCA Archive Interspeech 2020
ISCA Archive Interspeech 2020

Surfboard: Audio Feature Extraction for Modern Machine Learning

Raphael Lenain, Jack Weston, Abhishek Shivkumar, Emil Fristed

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.

doi: 10.21437/Interspeech.2020-2879

Cite as: Lenain, R., Weston, J., Shivkumar, A., Fristed, E. (2020) Surfboard: Audio Feature Extraction for Modern Machine Learning. Proc. Interspeech 2020, 2917-2921, doi: 10.21437/Interspeech.2020-2879

  author={Raphael Lenain and Jack Weston and Abhishek Shivkumar and Emil Fristed},
  title={{Surfboard: Audio Feature Extraction for Modern Machine Learning}},
  booktitle={Proc. Interspeech 2020},