基于合成数据和卷积神经网络的牛棚声事件检测

Yagya Raj Pandeya, Bhuwan Bhattarai, Joonwhoan Lee
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引用次数: 9

摘要

声音事件检测是几个应用领域的合理选择,如牛棚,茂密的森林,或任何黑暗的环境中,视觉对象通常模糊不清或看不见。本研究的目的是开发一种基于声音特征的大型牛场福利管理自主监测系统。本文编制了一个牛声人工数据集,并开发了一个用于数据标注的声事件标注工具。我们提出了一种卷积神经网络(CNN)架构用于罕见声音事件检测。所应用的目标检测方法比以往的相关研究获得了更高的定量评价分数和更精确的定性结果。最后,我们得出结论,基于CNN的稀有声音目标检测架构可以成为家庭福利管理的一种解决方案。事实上,人工数据准备策略可以很好地解决稀有声事件检测中的数据稀缺性问题和标注困难。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sound Event Detection in Cowshed using Synthetic Data and Convolutional Neural Network
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.
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