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An Always-On tinyML Acoustic Classifier for Ecological Applications

Sabbella, HR and Nair, AR and Gumme, V and Yadav, SS and Chakrabartty, S and Thakur, CS (2022) An Always-On tinyML Acoustic Classifier for Ecological Applications. In: 2022 IEEE International Symposium on Circuits and Systems, ISCAS 2022, 27 May - 1 June 2022, Austin, pp. 2393-2396.

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Official URL: https://doi.org/10.1109/ISCAS48785.2022.9937827


Long-term monitoring and tracking of wildlife and endangered species in their natural environment is challenging due to human factors and logistical limitations. We present a light-weight, always-on acoustic classification system that can identify the density of specific wildlife species in an ecological environment where human presence may be undesirable. The system uses a template-based support-vector-machine (SVM) classifier that combines acoustic filtering and classification into an in-filter computing and a hardware-friendly platform. We demonstrate the system's capabilities for identifying the density of different bird species using ARM Cortex-M4 based AudioMoth hardware. The embedded software, designed specifically for the AudioMoth hardware, can generate the programmable parameters, given limited training samples corresponding to different wildlife species. We show that the system can identify four different bird species with an accuracy of more than 95 and consumes a memory footprint of 14 KB SRAM and 149 KB Flash memory that can run for 48 days on battery without any human intervention. © 2022 IEEE.

Item Type: Conference Paper
Publication: Proceedings - IEEE International Symposium on Circuits and Systems
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Birds; Conservation; Ecology; Flash memory; Static random access storage; System-on-chip, Bird species; Endangered species; Long term monitoring; Long-term tracking; Monitoring and tracking; Natural environments; Support vectors machine; Template-support-vector-machine; Tinyml; Wildlife species, Support vector machines
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited: 04 Jan 2023 04:39
Last Modified: 04 Jan 2023 04:39
URI: https://eprints.iisc.ac.in/id/eprint/78682

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