Time-warping estimation improves signal analysis with faster computing
Time-warping estimation via stationarity-based learning of the de-warped signal
Machine Learning
Summary
Time-warping is like adjusting the speed of a recording to line up important events. The authors created a new method called TWET that uses a smart wavelet approach to guess how a signal has been stretched or squished in time from just one example. This method teaches a computer model to find these changes quickly and accurately by looking for stability patterns in the signal. Compared to older methods, TWET is faster and better at fixing these time changes, making it useful for things like medical or animal sound analysis.
What this means in practice
- •For medical imaging analysts: Correct timing distortions in biomedical signals quickly to improve diagnostic accuracy.
- •For bioacoustics engineers: Enhance animal sound recordings by estimating and reversing time-warp effects for clearer analysis.
Authors
Corentin Presvôts, Adrien Meynard
Abstract
Time-warping estimation is a fundamental problem in signal processing with applications in bioacoustics, radar, and biomedical analysis. This paper introduces a Time-Warping Estimation Trainable (TWET) model for estimating timewarping functions from a single observation. The proposed approach formulates time-warping estimation as a stationarization problem in the wavelet domain and leverages a hierarchical dilated convolutional architecture to estimate the time-warping functions. A differentiable stationarity criterion is introduced for end-to-end optimization. TWET is compared with existing approaches. Experimental results show improved deformation reconstruction accuracy together with significantly reduced computation time, making the framework compatible with low-latency applications.