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are varied but include techniques such as drawing atten- tion to portions of the input signal that were responsible for strong activations of a neural network.
Another exciting avenue of machine learning research is to utilize systems that take advantage of physical knowl- edge. An example of this can be seen in Raissi et al. (2019) who trained deep neural networks with priors that were grounded in the physics of problem domains. One can envision acoustic systems that have prior knowledge about e.g., transmission loss and channel characteristics, and such systems may be a promising area for future research.
We hope that this “gentle” introduction to machine learn- ing will inspire readers to dig deeper into the possible uses of machine learning in their own acoustics problems. The Journal of the Acoustical Society published a special issue in 2021 on the use of machine learning in acoustics, and this collection of papers provides a wide range of exam- ple applications including medical applications, speech, oceanography, bioacoustics, and music. We hope that this collection stimulates the wider adoption of machine learning within the field of acoustics. There are a growing number of published acoustics papers that use these tech- niques, and it is likely that machine learning will become a valuable component in the acoustician’s toolkit.
Acknowledgments
This work was supported by Office of Naval Research Awards N00014-17-1-2867 and N00014-20-1-2029. We thank Arthur Popper for his valuable suggestions in the
development of this manuscript.
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