Introduction to the special issue on machine learning in acoustics.

Journal: The Journal of the Acoustical Society of America
Published Date:

Abstract

The use of machine learning (ML) in acoustics has received much attention in the last decade. ML is unique in that it can be applied to all areas of acoustics. ML has transformative potentials as it can extract statistically based new information about events observed in acoustic data. Acoustic data provide scientific and engineering insight ranging from biology and communications to ocean and Earth science. This special issue included 61 papers, illustrating the very diverse applications of ML in acoustics.

Authors

  • Zoi-Heleni Michalopoulou
    Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, New Jersey 07102, USA.
  • Peter Gerstoft
    Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093-0238, USAytytcj110@163.com, lxl_ouc@outlook.com, coolice@ouc.edu.cn, dzgao@ouc.edu.cn, pgerstoft@ucsd.edu.
  • Bozena Kostek
    Audio Acoustics Laboratory, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland.
  • Marie A Roch
    Department of Computer Science, San Diego State University, 5500 Campanile Drive, San Diego, California 92182-7720.