  We train an object detector built from convolutional neural networks to count
interference fringes in elliptical antinode regions visible 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, "SPNet" are intended 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 relatively small number of human-annotated
images, we also train on a large corpus of synthetic images whose visual
properties have been matched to those of the real images by using a Generative
Adversarial Network to perform style transfer. Applying the model to predict
annotations of thousands of unlabeled video frames, we can track features and
measure oscillations consistent with audio recordings of the same drum strikes.
One surprising result is that the machine-annotated video frames reveal
transitions between the first and second harmonics of drum notes that
significantly precede such transitions present in the audio recordings. As this
paper primarily concerns the development of the model, deeper physical insights
await its further application.
