By Roger Lee (eds.)
This edited e-book offers clinical result of the 15th IEEE/ACIS overseas convention on desktop and data technological know-how (ICIS 2016) which used to be hung on June 26– 29 in Okayama, Japan. the purpose of this convention used to be to collect researchers and scientists, businessmen and marketers, lecturers, engineers, machine clients, and scholars to debate the various fields of machine technology and to percentage their studies and trade new principles and data in a significant method. study effects approximately all elements (theory, purposes and instruments) of machine and data technology, and to debate the sensible demanding situations encountered alongside the best way and the strategies followed to resolve them.
The convention organizers chosen the easiest papers from these papers authorised for presentation on the convention. The papers have been selected in accordance with evaluate rankings submitted through participants of this system committee, and underwent extra rigorous rounds of evaluate. 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 sphere of computing device and knowledge science.
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Additional info for Computer and Information Science
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.
Second, because of non-negativity constraint, we added a constant value to DCT, which is extracted from lip images. This scheme also degraded the conversion performance of VC. 1 Dictionary Learning In order to construct a compact dictionary, a parallel dictionary between the source and target speakers is estimated using parallel-constrained matrix factorization. Figure 3 shows the ﬂow of parallel dictionary learning for multimodal VC. ????sa ∈ ℝ+ , ????sv ∈ ℝ± , and ????ta ∈ ℝ+ denote input the source speaker’s audio exemplar, the source speaker’s visual exemplar, and the target speaker’s audio exemplar, respectively.
Audio Speech Lang. Process. 19(7), 2067–2080 (2011) 10. : Voice conversion using partial least squares regression. IEEE Trans. Audio Speech Lang. Process. 18, 912–921 (2010) 11. : Probabilistic latent semantic indexing. In: Proceedings of the SIGIR, pp. 50–57 (1999) 12. : Spectral voice conversion for text-to-speech synthesis. In: ICASSP, pp. 285–288 (1998) 13. : STRAIGHT, exploitation of the other aspect of vocoder: perceptually isomorphic decomposition of speech sounds. Acoust. Sci. Technol. 349–353 (2006) 14.