The work presented in this paper focuses on the use of acoustic systems for passive acoustic monitoring of ocean vitality for fish populations. Specifically, it focuses on the use of acoustic systems for passive acoustic monitoring of ocean vitality for fish populations. To this end, various indicators can be used to monitor marine areas such as both the geographical and temporal evolution of fish populations. A discriminative model is built using supervised machine learning (random-forest and support-vector machines). Each acquisition is represented in a feature space, in which the patterns belonging to different semantic classes are as separable as possible. The set of features proposed for describing the acquisitions come from an extensive state of the art in various domains in which classification of acoustic signals is performed, including speech, music, and environmental acoustics. Furthermore, this study proposes to extract features from three representations of the data (time, frequency, and cepstral domains). The proposed classification scheme is tested on real fish sounds recorded on several areas, and achieves 96.9% correct classification compared to 72.5% when using reference state of the art features as descriptors. The classification scheme is also validated on continuous underwater recordings, thereby illustrating that it can be used to both detect and classify fish sounds in operational scenarios.
Skip Nav Destination
Article navigation
May 2018
May 11 2018
Automatic fish sounds classification
Marielle Malfante;
Marielle Malfante
a)
1
Institute of Engineering University Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab
, 38000 Grenoble, France
Search for other works by this author on:
Jérôme I. Mars;
Jérôme I. Mars
1
Institute of Engineering University Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab
, 38000 Grenoble, France
Search for other works by this author on:
Mauro Dalla Mura;
Mauro Dalla Mura
1
Institute of Engineering University Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab
, 38000 Grenoble, France
Search for other works by this author on:
Cédric Gervaise
Cédric Gervaise
2
Chorus, Fondation Grenoble INP
, 38000 Grenoble, France
Search for other works by this author on:
a)
Electronic mail: [email protected]
J. Acoust. Soc. Am. 143, 2834–2846 (2018)
Article history
Received:
July 07 2017
Accepted:
April 13 2018
Citation
Marielle Malfante, Jérôme I. Mars, Mauro Dalla Mura, Cédric Gervaise; Automatic fish sounds classification. J. Acoust. Soc. Am. 1 May 2018; 143 (5): 2834–2846. https://doi.org/10.1121/1.5036628
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Citing articles via
All we know about anechoic chambers
Michael Vorländer
A survey of sound source localization with deep learning methods
Pierre-Amaury Grumiaux, Srđan Kitić, et al.
Does sound symbolism need sound?: The role of articulatory movement in detecting iconicity between sound and meaning
Mutsumi Imai, Sotaro Kita, et al.
Related Content
Automatic fish sounds classification
J Acoust Soc Am (April 2016)
Identification of fish species in estuaries and rivers using recorded soundscape with supervised machine learning
J Acoust Soc Am (September 2018)
Automatic detection of fish sounds based on multi-stage classification including logistic regression via adaptive feature weighting
J. Acoust. Soc. Am. (November 2018)
The effects of underwater noise on marine mammals.
J Acoust Soc Am (April 2011)