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Time-warping estimation via stationarity-based learning of the de-warped signal
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 a
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- arXiv · AI, language, vision and robotics · 2026-09-15T08:02:58.000Z
First collected: 2026-09-20T09:01:24.920Z. This is not the publication date.