  Acoustic data provide scientific and engineering insights in fields ranging
from biology and communications to ocean and Earth science. We survey the
recent advances and transformative potential of machine learning (ML),
including deep learning, in the field of acoustics. ML is a broad family of
techniques, which are often based in statistics, for automatically detecting
and utilizing patterns in data. Relative to conventional acoustics and signal
processing, ML is data-driven. Given sufficient training data, ML can discover
complex relationships between features and desired labels or actions, or
between features themselves. With large volumes of training data, ML can
discover models describing complex acoustic phenomena such as human speech and
reverberation. ML in acoustics is rapidly developing with compelling results
and significant future promise. We first introduce ML, then highlight ML
developments in four acoustics research areas: source localization in speech
processing, source localization in ocean acoustics, bioacoustics, and
environmental sounds in everyday scenes.
