A Chainsaw-Sound Recognition Model for Detecting Illegal Logging Activities in Forests

Daniel Simiyu, Allan Vikiru, Henry Muchiri, Fengshou Gu, Julius Butime

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Illegal logging activities in Kenya results to an increase in carbon emissions, creating a need to detect and prevent illegal logging activities. This paper proposes the use of an internet-of-things (IoT) based architecture for detection of logging sounds by chainsaw and a machine learning (ML) technique to identify and classify the collected environmental sounds. The IoT architecture, based on Long-Range (LoRa) wireless technology, will include devices fitted with sound sensors that are strategically placed in an identified site within the forest. Sound signals will then be transmitted in real-time to a cloud-based platform for storage, and classification using a temporal frequency convolutional neural network (TFCNN) model. The TFCNN model will include an attention mechanism for recognition of different sounds by their distinct characteristics and a feature representation module to further distinguish chainsaw sounds from other environmental sounds. Open-source datasets such as ESC-50 and FSC22 will be considered in model training but the latter will be utilized more due to its overall focus on forest acoustics.

Original languageEnglish
Title of host publicationProceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2
EditorsAndrew D. Ball, Huajiang Ouyang, Jyoti K. Sinha, Zuolu Wang
PublisherSpringer, Cham
Pages797-806
Number of pages10
Volume152
ISBN (Electronic)9783031494215
ISBN (Print)9783031494208, 9783031494239
DOIs
Publication statusPublished - 29 May 2024
EventThe UNIfied Conference of DAMAS, InCoME and TEPEN Conferences - Huddersfield, United Kingdom, Huddersfield, United Kingdom
Duration: 29 Aug 20231 Sep 2023
https://unified2023.org/

Publication series

NameMechanisms and Machine Science
PublisherSpringer
Volume152 MMS
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992

Conference

ConferenceThe UNIfied Conference of DAMAS, InCoME and TEPEN Conferences
Abbreviated titleUNIfied 2023
Country/TerritoryUnited Kingdom
CityHuddersfield
Period29/08/231/09/23
Internet address

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