Detecting right whales from autonomous surface vehicles using RNNs and CNNs

Vickers, William, Milner, Ben, Lines, Jason ORCID: https://orcid.org/0000-0002-1496-5941 and Lee, Robert (2019) Detecting right whales from autonomous surface vehicles using RNNs and CNNs. In: UNSPECIFIED.

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Abstract

This work is concerned with the problem of detecting right whales from autonomous surface vehicles (ASVs) and investigates the effectiveness of a range of deep learning methods. Given the success of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) across many different applications, these form the basis for comparison. From the input audio, power spectral features are extracted and consideration is given to how their time resolution and frequency resolution affects the detection accuracy and the number of points that need to be processed which is an important consideration within the limited processing power on an ASV. The effect of downsampling the input audio before feature extraction is also investigated. Tests establish that CNNs consistently give best performance on the detection task with accuracy of over 92% compared to around 90% with RNNs. Furthermore, tests measuring the processing time for detection found the CNN to be three times faster than the RNN

Item Type:	Conference or Workshop Item (Paper)
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and Statistics
Depositing User:	LivePure Connector
Date Deposited:	08 Jul 2019 13:30
Last Modified:	20 Jun 2023 14:37
URI:	https://ueaeprints.uea.ac.uk/id/eprint/71652
DOI:

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