用声音识别蜂巢状态，音频图像选对效果翻倍
Convolutional Neural Network Optimization for Beehive Classification Using Bioacoustic Signals
- 用时频图像+卷积网络分析蜂群声音
- 耳蜗图表现最佳，准确率达98.31%
- 模型压缩91.8%，推理提速66%，适合实时部署
蜜蜂数量行为是生态学重要现象，不仅关乎蜂蜜和蜂蜡生产，也影响周围动植物繁衍。本研究通过非侵入式监听蜂巢声音信号，利用多种时频图像表示（如频谱图、梅尔频谱图、平滑频谱图和耳蜗图）结合卷积神经网络，实现蜂巢状态分类与监测。结果表明，耳蜗图在未见过的数据上达到98.31%的准确率，优于其他表示方法。进一步采用剪枝、量化和知识蒸馏等优化策略，使模型尺寸减少91.8%，推理时间加速66%，显著提升其在实时应用中的可行性。研究强调了选择合适时频表示及模型优化对降低部署难度、加快推理速度的重要性。
原文摘要 · Abstract (English)
The behavior of honeybees is an important ecological phenomenon not only in terms of honey and beeswax production but also due to the proliferation of flora and fauna around it. The best way to study this significant phenomenon is by non-invasive monitoring of beehives using the sounds produced by various body movements that give out audio signals which can be exploited for various predictions related to the objectives mentioned above. This study investigates the application of Convolutional Neural Networks to classify and monitor different hive states with the help of joint time and frequency image representations such as Spectrogram, Mel-Spectrogram, Smoothed-Spectrogram, and Cochleagram. Our findings indicate that the Cochleagram outperformed all the other representations, achieving an accuracy of 98.31% on unseen data. Furthermore, we employed various strategies including pruning, quantization, and knowledge distillation to optimize the network and prevent any potential issues with model size. With these optimizations, the network size was lowered by 91.8% and the inference time was accelerated by 66%, increasing its suitability for real-time applications. Thus our study emphasizes the significance of using optimization approaches to minimize model size, avoid deployment problems, and expedite inference for real-time application as well as the selection of an appropriate time-frequency representation for optimal performance.
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