Tutorial on machine learning for acoustics

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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. We have ML examples from ocean acoustics, room acoustics, and personalized spatial audio. For room acoustics, we take room impulse responses (RIR) generation as an example application. For personalized spatial audio, we take head-related transfer function (HRTF) up-sampling as examples. The tutorial will conclude with a set of Jupiter notebook examples on GitHub demonstrating ML benefits.