A noise-robust acoustic method for recognition of foraging activities of grazing cattle
| Autor: | Martinez-Rau, Luciano S., Chelotti, José O., Ferrero, Mariano, Galli, Julio R., Utsumi, Santiago A., Planisich, Alejandra M., Rufiner, H. Leonardo, Giovanini, Leonardo L. |
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| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Sound (cs.SD) Audio and Speech Processing (eess.AS) FOS: Electrical engineering electronic engineering information engineering Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing Machine Learning (cs.LG) |
| DOI: | 10.48550/arxiv.2304.14824 |
| Popis: | To stay competitive in the growing dairy market, farmers must continuously improve their livestock production systems. Precision livestock farming technologies provide individualised monitoring of animals on commercial farms, optimising livestock production. Continuous acoustic monitoring is a widely accepted sensing technique used to estimate the daily rumination and grazing time budget of free-ranging cattle. However, typical environmental and natural noises on pasture noticeably affect the performance and generalisation of current acoustic methods. In this study, we present an acoustic method called Noise-Robust Foraging Activity Recognizer (NRFAR). The proposed method determines foraging activity bouts by analysing fixed-length segments of identified jaw movement events associated with grazing and rumination. The additive noise robustness of NRFAR was evaluated for several signal-to-noise ratios, using stationary Gaussian white noise and four different non-stationary natural noise sources. In noiseless conditions, NRFAR reaches an average balanced accuracy of 89%, outperforming two previous acoustic methods by more than 7%. Additionally, NRFAR presents better performance than previous acoustic methods in 66 out of 80 evaluated noisy scenarios (p list of used audio-clips is available in the list_audio_clips.xlsx |
| Databáze: | OpenAIRE |
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