JOURNAL ARTICLE
RESEARCH SUPPORT, NON-U.S. GOV'T
Add like
Add dislike
Add to saved papers

Classification of mysticete sounds using machine learning techniques.

Automatic classification of mysticete sounds has long been a challenging task in the bioacoustics field. The unknown statistical properties of the signals as well as the use of different recording apparatus and low signal-to-noise ratio conditions often lead to non-optimal systems. The goal of this paper is to design methods for the automatic classification of mysticete sounds using a restricted Boltzmann machine and a sparse auto-encoder that are widely used in the field of artificial intelligence. Experiments on five species of mysticetes are presented. The different methods are employed on the subset of species whose frequency range overlaps, as well as in all five species' calls. Moreover, results are offered with and without the use of a noise class. Overall, the systems are able to achieve an average classification accuracy of over 69% (with noise) and 80% (without noise) given the different architectures.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app