用于计数的卷积神经网络：钢鼓瞬态现象的目标检测
ConvNets for Counting: Object Detection of Transient Phenomena in Steelpan Drums
AI总结：
本研究构建卷积神经网络目标检测器计数钢鼓干涉条纹，结合众包标注数据与风格迁移合成数据训练评估，发现高八度音共振振荡早于对应二次谐波声强上升，为钢鼓时变行为研究提供模型工具。
摘要：
我们训练了一个基于卷积神经网络构建的目标检测器，用于对加勒比钢鼓瞬态振荡的高速视频帧中的椭圆波腹区域内的干涉条纹进行计数，这些视频由电子散斑干涉法（ESPI）拍摄。该模型提供的标注旨在通过追踪共振振动模式的演化，助力理解这类钢鼓的时变行为。该系统在众包人工标注图像数据集上训练，数据集来自Zooniverse Steelpan Vibrations项目。由于人工标注图像数量少且标注任务存在模糊性，我们还在大量合成图像语料上评估了模型，这些合成图像通过生成对抗网络进行风格迁移，属性已与真实图像匹配。将模型应用于数千张未标注视频帧后，我们测得的振荡与这些鼓敲击的音频记录一致。一个意外结果是，高八度音的共振振荡显著早于对应二次谐波音调的声强上升，其背后的机制尚不明确。本文主要关注预测模型的开发；钢鼓图像的进一步探索及更深层的物理洞见有待模型的进一步应用。
英文摘要：
We train an object detector built from convolutional neural networks to count interference fringes in elliptical antinode regions in frames of high-speed video recordings of transient oscillations in Caribbean steelpan drums illuminated by electronic speckle pattern interferometry (ESPI). The annotations provided by our model aim to contribute to the understanding of time-dependent behavior in such drums by tracking the development of sympathetic vibration modes. The system is trained on a dataset of crowdsourced human-annotated images obtained from the Zooniverse Steelpan Vibrations Project. Due to the small number of human-annotated images and the ambiguity of the annotation task, we also evaluate the model on a large corpus of synthetic images whose properties have been matched to the real images by style transfer using a Generative Adversarial Network. Applying the model to thousands of unlabeled video frames, we measure oscillations consistent with audio recordings of these drum strikes. One unanticipated result is that sympathetic oscillations of higher-octave notes significantly precede the rise in sound intensity of the corresponding second harmonic tones; the mechanism responsible for this remains unidentified. This paper primarily concerns the development of the predictive model; further exploration of the steelpan images and deeper physical insights await its further application.
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