Individual identification in acoustic recordings.

Recent advances in bioacoustics combined with acoustic individual identification (AIID) could open frontiers for ecological and evolutionary research because traditional methods of identifying individuals are invasive, expensive, labor-intensive, and potentially biased. Despite overwhelming evidence that most taxa have individual acoustic signatures, the application of AIID remains challenging and uncommon. Furthermore, the methods most commonly used for AIID are not compatible with many potential AIID applications.

Tracking transients in steelpan strikes using surveillance technology.

This paper presents advancements in tracking features in high-speed videos of Caribbean steelpans illuminated by electronic speckle pattern interferometry, made possible by incorporating robust computer vision libraries for object detection and image segmentation, and cleaning of the training dataset. Besides increasing the accuracy of fringe counts by 10% or more compared to previous work, this paper introduces a segmentation-regression map for the entire drum surface yielding interference fringe counts comparable to those obtained via object detection. Once trained, this model can count fringes for musical instruments not part of the training set, including those with non-elliptical antinode shapes.

ConvNets for counting: Object detection of transient phenomena in steelpan drums.

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.