Translation Between Waves, wave2wave
Tsuyoshi Okita, Hirotaka Hachiya, Sozo Inoue, Naonori Ueda

TL;DR
This paper introduces a novel neural translation model for continuous sensor signal waves, employing window-based representations and iterative back-translation to improve performance on real-world earthquake and activity data.
Contribution
It presents a new seq2seq variant with adaptive wave representation and iterative back-translation for better modeling of continuous sensor signals.
Findings
46% test loss reduction on 1D data
1625% perplexity improvement on high-dimensional data
Effective translation of real-world sensor signals
Abstract
The understanding of sensor data has been greatly improved by advanced deep learning methods with big data. However, available sensor data in the real world are still limited, which is called the opportunistic sensor problem. This paper proposes a new variant of neural machine translation seq2seq to deal with continuous signal waves by introducing the window-based (inverse-) representation to adaptively represent partial shapes of waves and the iterative back-translation model for high-dimensional data. Experimental results are shown for two real-life data: earthquake and activity translation. The performance improvements of one-dimensional data was about 46% in test loss and that of high-dimensional data was about 1625% in perplexity with regard to the original seq2seq.
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Taxonomy
TopicsAlgorithms and Data Compression · Computational Physics and Python Applications
MethodsTanh Activation · Sigmoid Activation · Long Short-Term Memory · Sequence to Sequence
