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Title
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Bat echolocation scan pattern reconstruction using convolutional sparse coding
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Author
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Abstract
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| 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. |
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Language
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English
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Source (book)
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2024 IEEE Applied Sensing Conference (APSCON), 22-24 January, 2024, Goa, India
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Publication
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2024
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ISBN
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979-83-503-1727-5
979-83-503-1728-2
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DOI
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10.1109/APSCON60364.2024.10466208
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Volume/pages
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p. 1-4
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ISI
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001195436600106
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Full text (Publisher's DOI)
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Full text (open access)
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The author-created version that incorporates referee comments and is the accepted for publication version Available from 19.09.2024
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Full text (publisher's version - intranet only)
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