Sound Event Detection in Cowshed using Synthetic Data and Convolutional Neural Network

Abstract

The sound event detection is a reasonable choice for several application domains like cattle shed, dense forest, or any dark environment where the visual object usually obscured or unseen. The aim of this study is the development of an autonomous monitoring system for welfare management in large cow farms based on sound characteristics. In this paper, we prepare a cow sound artificial dataset and develop a sound event annotation tool for annotation of data. We propose a convolutional neural network (CNN) architecture for rare sound event detection. The applied object detection method achieves a higher quantitative evaluation score and a more precise qualitative result than the past related study. Finally, we conclude that the CNN based architecture for rare sound object detection can be one solution for domestic welfare management. Indeed, the artificial data preparation strategy can be a way to deal with the data scarcity problem and annotation difficulties for rare sound event detection.