Translation Between Waves, wave2wave
This addresses data scarcity in real-world sensor applications, though it appears incremental as an adaptation of existing seq2seq methods.
The paper tackles the opportunistic sensor problem by proposing a new seq2seq variant for translating continuous signal waves, achieving performance improvements of 46% in test loss for 1D data and 1625% in perplexity for high-dimensional data compared to the original seq2seq.
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.