  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.
