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Dynamic time warping estimates the alignment between two sequences and is designed to handle a variable amount of time warping. In many contexts, it performs poorly when confronted with two sequences of different scale, in which the average slope of the true alignment path in the pairwise cost matrix deviates significantly from one. This paper investigates ways to improve the robustness of DTW to such global time warping conditions, using an audio–audio alignment task as a motivating scenario of interest. We modify a dataset commonly used for studying audio–audio synchronization in order to construct a benchmark in which the global time warping conditions are carefully controlled, and we evaluate the effectiveness of several strategies designed to handle global time warping. Among the strategies tested, there is a clear winner: performing sequence length normalization via downsampling before invoking DTW. This method achieves the best alignment accuracy across a wide range of global time warping conditions, and it maintains or reduces the runtime compared to standard usages of DTW. We present experiments and analyses to demonstrate its effectiveness in both controlled and realistic scenarios.
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Trainable Time Warping: Aligning Time-series in the Continuous-time Domain
DTW calculates the similarity or alignment between two signals, subject to temporal warping. However, its computational complexity grows exponentially with the number of time-series. Although there have been algorithms developed that are linear in the number of time-series, they are generally quadratic in time-series length. The exception is generalized time warping (GTW), which has linear computational cost. Yet, it can only identify simple time warping functions. There is a need for a new fast, high-quality multisequence alignment algorithm. We introduce trainable time warping (TTW), whose complexity is linear in both the number and the length of time-series. TTW performs alignment in the continuoustime domain using a sinc convolutional kernel and a gradient-based optimization technique. We compare TTW and GTW on S5 UCR datasets in time-series averaging and classification. TTW outperforms GTW on 67.1% of the datasets for the averaging tasks, and 61.2% of the datasets for the classification tasks.
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- Award ID(s):
- 1651740
- PAR ID:
- 10125069
- Date Published:
- Journal Name:
- International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
- Page Range / eLocation ID:
- 3502 to 3506
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/icassp.2019.8682322
