  Removing background noise from speech audio has been the subject of
considerable effort, especially in recent years due to the rise of virtual
communication and amateur recordings. Yet background noise is not the only
unpleasant disturbance that can prevent intelligibility: reverb, clipping,
codec artifacts, problematic equalization, limited bandwidth, or inconsistent
loudness are equally disturbing and ubiquitous. In this work, we propose to
consider the task of speech enhancement as a holistic endeavor, and present a
universal speech enhancement system that tackles 55 different distortions at
the same time. Our approach consists of a generative model that employs
score-based diffusion, together with a multi-resolution conditioning network
that performs enhancement with mixture density networks. We show that this
approach significantly outperforms the state of the art in a subjective test
performed by expert listeners. We also show that it achieves competitive
objective scores with just 4-8 diffusion steps, despite not considering any
particular strategy for fast sampling. We hope that both our methodology and
technical contributions encourage researchers and practitioners to adopt a
universal approach to speech enhancement, possibly framing it as a generative
task.
