Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/31902
Title: Livestock vocalisation classification in farm soundscapes
Contributor(s): Bishop, James C  (author)orcid ; Falzon, Greg  (author)orcid ; Trotter, Mark  (author); Kwan, Paul  (author); Meek, Paul D  (author)
Publication Date: 2019-07
Early Online Version: 2019-05-06
Open Access: Yes
DOI: 10.1016/j.compag.2019.04.020
Handle Link: https://hdl.handle.net/1959.11/31902
Abstract: Livestock vocalisations have been shown to contain information related to animal welfare and behaviour. Automated sound detection has the potential to facilitate a continuous acoustic monitoring system, for use in a range Precision Livestock Farming (PLF) applications. There are few examples of automated livestock vocalisation classification algorithms, and we have found none capable of being easily adapted and applied to different species' vocalisations. In this work, a multi-purpose livestock vocalisation classification algorithm is presented, utilising audio-specific feature extraction techniques, and machine learning models. To test the multi-purpose nature of the algorithm, three separate data sets were created targeting livestock-related vocalisations, namely sheep, cattle, and Maremma sheepdogs. Audio data was extracted from continuous recordings conducted on-site at three different operational farming enterprises, reflecting the conditions of real deployment. A comparison of Mel-Frequency Cepstral Coefficients (MFCCs) and Discrete Wavelet Transform-based (DWT) features was conducted. Classification was determined using a Support Vector Machine (SVM) model. High accuracy was achieved for all data sets (sheep: 99.29%, cattle: 95.78%, dogs: 99.67%). Classification performance alone was insufficient to determine the most suitable feature extraction method for each data set. Computational timing results revealed the DWT-based features to be markedly faster to produce (14.81 - 15.38% decrease in execution time). The results indicate the development of a highly accurate livestock vocalisation classification algorithm, which forms the foundation for an automated livestock vocalisation detection system.
Publication Type: Journal Article
Source of Publication: Computers and Electronics in Agriculture, v.162, p. 531-542
Publisher: Elsevier BV
Place of Publication: Netherlands
ISSN: 1872-7107
0168-1699
Fields of Research (FoR) 2020: 460103 Applications in life sciences
Socio-Economic Objective (SEO) 2020: 220402 Applied computing
109902 Animal welfare
220403 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Environmental and Rural Science
School of Science and Technology

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