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基于深度转移学习的鸟类物种分类,使用Mel光谱图像进行分类
Mrinal Kanti Baowaly1, Bisnu Chandra Sarkar1, Md Abul Ala Walid2,3
1Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh.
PloS one
|August 12, 2024
概括
本研究介绍了一种基于音频的系统,用于使用深度转移学习对东非鸟类进行分类. 带有Gated Recurrent Unit的EfficientNet-B7模型实现了84.03%的准确性,改善了自动鸟类识别.
科学领域:
- 鸟类学 鸟类学是一门学科.
- 机器学习 机器学习
- 生物声学是一种生物声学.
背景情况:
- 传统的鸟类分类方法耗时,需要专家知识.
- 基于音频的分类为物种识别提供了一个自动化和高效的替代方案.
- 环境监测在很大程度上依赖于准确的鸟类物种分类.
研究的目的:
- 为264种东非物种开发基于音频的自动鸟类物种分类系统.
- 利用深度转移学习,特别是EfficientNet,提高分类准确性.
- 集成循环神经网络 (RNN) 来捕获时间音频模式.
主要方法:
- 使用经过修改的深度转移学习方法,使用预先训练的EfficientNet模型.
- 调整了EfficientNet以从鸟类声音的Mel光谱图像中学习模式.
- 结合精细调节的EfficientNet与Gated Recurrent Unit (GRU) 和长短期内存 (LSTM) 的RNN.
- 在约17000个鸟类声音记录的数据集上训练模型.
主要成果:
- 与GRU相结合的EfficientNet-B7模型实现了最高的性能.
- 这种模型的准确率达到84.03%.
- 获得了0.8342的宏观平均精度得分.
结论:
- 拟议的基于音频的系统有效地对东非鸟类进行了分类.
- 深度转移学习,特别是与GRU一起的EfficientNet-B7,显示了自动化生物声学监控的重大前景.
- 这种方法提高了鸟类调查的效率和准确性.
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