The application of deep learning to solving acoustic detection and identification challenges is a rapidly-evolving subfield of underwater acoustics. Automatic signal identification can be used for many applications, like enabling the
compilation of large datasets from many sources, which can be used to better constrain source-specific characteristics and trends. Earlier analyses (Garibbo et al., 2020) identified the different contributions of wind, weather, shipping
and earthquakes. The long-term acoustic measurements regularly include calls from fin whales, whose presence and vocal activities in the area vary with seasons; their 20-Hz calls are sometimes mixed with other signals, like
earthquakes or shipping. We present here the application of deep learning to automatically identify these whale calls. Percentile analyses of the temporal variation of the frequency of calls, their Power Spectral Density (PSD), and Sound
Pressure Level (SPL) is carried out to determine their respective contributions to the overall soundscape and highlight relevant information about these whale populations. The deep learning approaches selected here can also be
used for other types of animal vocalisations and for other short-term processes (e.g. passing ships, earthquakes of different types), assisting in their identification and in the statistical and temporal analyses of low-frequency
Original languageEnglish
Article number070021
Number of pages8
JournalProceedings of Meetings on Acoustics
Issue number1
Publication statusPublished - 2 Nov 2021
Event6th Underwater Acoustics Conference and Exhibition, UACE 2021 - Virtual, Online
Duration: 20 Jun 202125 Jun 2021

Bibliographical note

Funding Information:
This research is supported by the UK Engineering and Physical Sciences Research Council (EPSRC), as part of industrial Cooperative Award in Science and Technology (iCASE) project #2279119, supported by Defence Science & Technology Laboratory (Dstl) and Atomic Weapons Establishment (AWE).


  • acoustics
  • underwater acoustics
  • ambient noise
  • deep learning
  • automatic signal identification

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Artificial Intelligence


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