Publication
Title
Bat echolocation scan pattern reconstruction using convolutional sparse coding
Author
Abstract
Compressive sensing enables the detection and allocation of sparse signals at a sub-Nyquist sampling rate. For this reason, it is particularly interesting in the case of high sample rate applications. Monitoring bat echolocation signals using an ultrasonic microphone array is a high sample rate application. To evaluate the methods proposed in this work, Nyquist-compliant sample data is undersampled to simulate compressive sensing (CS) and reconstructed using convolutional sparse coding with a dictionary set trained on a bat’s echolocation calls. This paper evaluates the robustness of the proposed method for extracting key acoustic properties from bat echolocation signals. It compares these properties to those extracted with a Nyquist-compliant dataset that serves as a ground truth reference.
Language
English
Source (book)
2024 IEEE Applied Sensing Conference (APSCON), 22-24 January, 2024, Goa, India
Publication
2024
ISBN
979-83-503-1727-5
979-83-503-1728-2
DOI
10.1109/APSCON60364.2024.10466208
Volume/pages
p. 1-4
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
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Research group
Publication type
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Publications with a UAntwerp address
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Creation 02.04.2024
Last edited 03.04.2024
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