| Full text | |
| Author(s): |
Souza, Andre Moreira
;
Kobayashi, Livia Lissa
;
Tassoni, Lucas Andrietta
;
Garbossa, Cesar Augusto Pospissil
;
Ventura, Ricardo Vieira
;
de Sousa, Elaine Parros Machado
Total Authors: 6
|
| Document type: | Journal article |
| Source: | APPLIED INTELLIGENCE; v. 55, n. 7, p. 12-pg., 2025-05-01. |
| Abstract | |
The increasing demand for animal protein products has led to the emergence of Precision Livestock Farming (PLF) and the adoption of sensing technologies, big data solutions, and Machine Learning (ML) methods in modern livestock farming. At the same time, the audio signal processing field has undergone notable advancements in recent years, transitioning from traditional techniques to more sophisticated ML approaches, with open challenges in detecting and classifying complex, low-quality, and overlapping sounds in real-world scenarios. In this paper, we evaluate deep learning methods, conceived from computer vision to attention-based approaches, for Audio Event Detection (AED) on a novel audio dataset from a swine farming environment with challenging characteristics, such as weak annotations and high amounts of noise. The primary purpose of our study is to prospect effective AED solutions for the development of tools for auditing livestock farms, which could be used to improve animal welfare. Our results show that, despite inherent limitations in the dataset's size, class imbalance, and sound quality, Convolutional Neural Network (CNN) and attention-based architectures are respectively effective and promising for detecting complex audio events. Further research may explore avenues for optimizing model performance in similar, real-life datasets while simultaneously amplifying annotated events and reducing annotation costs, thereby enhancing the broader applicability of AED methods in diverse audio processing scenarios. (AU) | |
| FAPESP's process: | 16/19514-2 - Development of a genomic database for Nellore cattle and computational tools for implementing large-scale studies |
| Grantee: | Ricardo Vieira Ventura |
| Support Opportunities: | Research Grants - Young Investigators Grants |
| FAPESP's process: | 20/07200-9 - Analyzing complex data from COVID-19 to support decision making and prognosis |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD) |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Research Projects - Thematic Grants |