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深度学习的狗的语音分析:为生物声学研究开发一个全自动语音分析系统

Mahmut Karaaslan¹, Bahaeddin Turkoglu², Ersin Kaya¹

¹Department of Computer Engineering, Konya Technical University, 42250 Konya, Turkey.

Sensors (Basel, Switzerland)

|January 8, 2025

概括

这项研究引入了一种用于动物声音分析的自动化系统,使用深度学习来分类吠叫和叫等发声. 这提高了生物声学研究的效率.

科学领域:

生物声学和动物行为分析
机器学习在生态学中的应用
计算伦理学计算伦理学

背景情况:

手动分析动物发声是耗时且具有挑战性的.
了解动物行为和环境相互作用在很大程度上依赖于可靠的数据.
需要自动化系统来提高生物声学研究的效率.

研究的目的:

开发一种用于检测和分类动物声调的自动化系统.
使用音频数据提高行为分析的效率和准确性.
评估各种特征提取和深度学习模型的表现,以进行发音分类.

主要方法:

音频录音被预处理,以对相关的声音区域进行细分.
使用短时间里埃变换 (STFT),Mel频率塞普斯特拉系数 (MFCCs) 和线性频率塞普斯特拉系数 (LFCCs) 进行特征提取.
卷积神经网络 (CNN) 模型包括AlexNet,DenseNet,EfficientNet,ResNet50和ResNet152被用来进行分类.

主要成果:

自动化系统在分类动物的声音行为方面表现出高准确度,例如狗吠叫和叫.
不同的CNN模型和特征提取技术显示出不同程度的有效性.
该研究提供了一个强大的工具,通过声音进行高效和准确的行为分析.

结论:

自动化系统显著提高了生物声学研究的效率.
基于深度学习的方法为改善动物语音分类提供了有希望的途径.
使用先进的深度学习技术进行进一步的研究可以进一步完善行为分析.

关键词:

在美国,CNN是CNN.音频处理音频处理自动行为分析自动行为分析.生物声学是一种生物声学.

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