By Roger Lee (eds.)
This edited booklet offers clinical result of the 15th IEEE/ACIS foreign convention on laptop and data technology (ICIS 2016) which was once hung on June 26– 29 in Okayama, Japan. the purpose of this convention used to be to collect researchers and scientists, businessmen and marketers, academics, engineers, laptop clients, and scholars to debate the various fields of laptop technological know-how and to percentage their stories and alternate new rules and knowledge in a significant manner. examine effects approximately all elements (theory, purposes and instruments) of desktop and knowledge technological know-how, and to debate the sensible demanding situations encountered alongside the best way and the suggestions followed to unravel them.
The convention organizers chosen the simplest papers from these papers accredited for presentation on the convention. The papers have been selected in line with overview ratings submitted by means of participants of this system committee, and underwent additional rigorous rounds of evaluation. This e-book captures 12 of the conference’s so much promising papers, and we impatiently anticipate the $64000 contributions that we all know those authors will deliver to the sector of computing device and data science.
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In these approaches, the observed signal is represented by a linear combination of a small number of atoms, such as the exemplar and basis of NMF. In some approaches for source separation, the atoms are grouped for each source, and the mixed signals are expressed with a sparse representation of these atoms. By using only the weights of the atoms related to the target signal, the target signal can be reconstructed. Our exemplar-based multimodal VC has major two problems. First, because our method is exemplar-based, the dictionary is over-compete and contains a large number of bases.
Because of noise robustness of visual features, multimodal feature has been attracted in the ﬁeld of speech processing, and we have proposed multimodal VC using Non-negative Matrix Factorization (NMF). Experimental results showed that our conventional multimodal VC can eﬀectively converted in a noisy environment, however, the diﬀerence of conversion quality between audio input VC and multimodal VC is not so large in a clean environment. We assume this is because our exemplar dictionary is over-complete.
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