Selective audio recording device for wildlife research using embedded machine learning

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• dc.contributor.author Azziz, Julia
• dc.contributor.author Lema, Josefina
• dc.contributor.author Steinfeld, Leonardo
• dc.contributor.author Acevedo, Emiliano
• dc.contributor.author Rocamora, Martín
• dc.date.accessioned 2025-09-04T06:41:02Z
• dc.date.embargoEnd info:eu-repo/date/embargoEnd/2027-04-23
• dc.date.issued 2025
• dc.description.abstract Wildlife monitoring through sound recording has become an essential tool in ecological research. However, challenges such as limited power and memory constraints hinder large-scale, long-term deployment of monitoring devices. To address these limitations, this paper presents a novel wildlife monitoring device that integrates embedded machine learning (ML) for event-triggered recording. This system captures only relevant sounds, leading to a more efficient memory usage and power consumption than the traditional fixed-schedule scheme, and a significantly larger percentage of useful data collected. The device features a low-cost, low-power hardware design equipped with a digital microphone, dual MicroSD storage, and a flexible power system. Its embedded ML component enables real-time audio classification and selective recording triggered by specific acoustic events. Preliminary testing using a prototype device demonstrated effective detection of penguin vocalizations, achieving an average current intake ranging from 4.06 to 6.02 mA, depending on the operational mode. This enables the device to be powered by a small, cost-effective rechargeable battery and solar power, supporting near-perpetual operation. The proposed system represents a step forward in deploying low-cost, low-power, scalable devices for acoustic wildlife monitoring.
• dc.embargo.liftdate 2027-04-23
• dc.format.mimetype application/pdf
• dc.identifier.citation Azziz J, Lema J, Steinfeld L, Acevedo E, Rocamora M. Selective audio recording device for wildlife research using embedded machine learning. In: 2025 IEEE Latin Conference on IoT (LCIoT); 2025 April 23-25; Fortaleza, Brazil. p. 65-8. DOI: 10.1109/LCIoT64881.2025.11118542.
• dc.identifier.uri http://hdl.handle.net/10230/71108
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof 2025 IEEE Latin Conference on IoT (LCIoT); 2025 April 23-25; Fortaleza, Brazil.
• dc.rights © 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/LCIoT64881.2025.11118542
• dc.rights.accessRights info:eu-repo/semantics/embargoedAccess
• dc.subject.keyword Wildlife
• dc.subject.keyword Wildlife monitoring
• dc.subject.keyword Sound recording
• dc.subject.keyword Embedded machine learning
• dc.subject.keyword Real-time audio classification
• dc.title Selective audio recording device for wildlife research using embedded machine learning
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion