Time-warping estimation via stationarity-based learning of the de-warped signal

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.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2609.16796 [stat.ML]

(or arXiv:2609.16796v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2609.16796 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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