Visual Object Detector for Cow Sound Event Detection

Yagya Raj Pandeya,Bhuwan Bhattarai,Joonwhoan Lee

doi:10.1109/access.2020.3022058

Yagya Raj Pandeya, Bhuwan Bhattarai + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.3022058

Copy DOI

Abstract

Sound event detection (SED) is a reasonable choice in a number of application domains including cattle sheds, dense forests, or any dark environments where visual objects are usually concealed or invisible. This study presents an autonomous monitoring system based on sound characteristics developed for welfare management in large cattle farms. Two types of artificial audio datasets are prepared: the cow sound event dataset and the UrbanSound8K dataset, which are then used with various sound object detectors for real world implementation. Using a data-driven approach, a conventional convolutional neural network structure with certain improvements is first applied, and from there proceed to a two-stage visual object detection method for audio by treating acoustic signals as an RGB images. The object detection method achieves a higher quantitative evaluation score and more precise qualitative results than previous related studies. We conclude that visual object detection methods are more effective than currently-available CNN architectures for rare sound object detection. Indeed, an artificial data preparation strategy can provide a better method for addressing the problem of data scarcity and the annotation difficulties involved in rare sound event detection.

Highlights

Cattle behavior analysis has been an important issue since the earliest human civilizations
We were dealing with a hard-annotated dataset and wanted to compare the basic Convolutional neural networks (CNNs) modality with visual object detection to investigate whether the novel visual detectors were applicable to R-sound event detection (SED) or not
The results show that the proposed CNN performed 3% better than previous methods in terms of F1 score evaluation

Summary

Introduction

Cattle behavior analysis has been an important issue since the earliest human civilizations. The housing, feeding, and management of cattle determines their wellbeing. Such wellbeing contributes to their behavior and impacts their health and productivity. Compared to other grazing animals, cattle in restricted environment have relatively high stereotypic behaviors and it is necessary to observe closely their behavior and take the necessary steps to ensure their health and wellbeing. Cattle express their condition through their behavior and by producing sounds. The study of cattle’s wellbeing is a generally broad area of research and our focus here is exclusively on sound event detection (SED) for cattle in a restricted environment.

Objectives

Methods

Findings

Conclusion