ConvNets for Counting: Object Detection of Transient Phenomena in Steelpan Drums
This work addresses the problem of analyzing transient vibrations in steelpan drums for researchers in acoustics and materials science, but it is incremental as it applies existing object detection methods to a new domain-specific dataset.
The researchers trained a convolutional neural network object detector to count interference fringes in steelpan drum oscillations from high-speed video, enabling measurement of oscillations consistent with audio recordings and revealing that sympathetic oscillations precede sound intensity rises.
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.