By Pedro Gómez-Vilda, Victoria Rodellar-Biarge, Víctor Nieto-Lluis, Cristina Muñoz-Mulas (auth.), Carlos M. Travieso-González, Jesús B. Alonso-Hernández (eds.)
This ebook constitutes the lawsuits of the fifth overseas convention on Nonlinear Speech Processing, NoLISP 2011, held in Las Palmas de Gran Canaria, Spain, in November 2011. the aim of the workshop is to offer and talk about new principles, concepts and effects regarding replacement techniques in speech processing which can go away from the most move.
The 33 papers offered including 2 keynote talks have been rigorously reviewed and chosen for inclusion during this publication. the subjects of NOLISP 2011 have been non-linear approximation and estimation; non-linear oscillators and predictors; higher-order records; self sustaining part research; nearest acquaintances; neural networks; choice timber; non-parametric versions; dynamics of non-linear structures; fractal tools; chaos modeling; and non-linear differential equations.
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Formal tools were confirmed because the rigorous engineering method for the process improvement. utilizing formal ways to a wide and complicated approach improvement usually calls for the modelling of other elements of one of these method. for example, advanced platforms (such as built-in avionics structures, engine keep watch over software program) can contain sensible and timing necessities that needs to be ultimately applied as executing code on a speaking dispensed topology.
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J), n ˜ i (t) is the additive noise, and L is the length of the mixing ﬁlter. In the TF domain, the model (1) can be expressed as: J xif t = aijf sjf t + nif t , or equivalently X f = Af S f + N f , (2) j=1 where X f = [xif t ]f ∈ CI×T , Af = [aijf ]f ∈ CI×J , S f = [sjf t ]f ∈ CJ×T , N f = [nif t ]f ∈ CI×T , and f = 1, . . , F is the index of a frequency bin. According to [3, 5, 6], the random variable sjf t can be modeled as crf t , sjf t = (3) r∈Rj 2 where crf t ∼ Nc (0, σrf t ) are the realizations of independent proper complex zero-mean Gaussian random variables, and Rj is the set of indices of latent 2 components for the j-th source.
5 Conclusion By precise estimation of the LPEs according to the evaluation of the degree of local reconstructibility, we showed that it is possible to recognize the most informative subset inside the speech signal, called the UPM. We successfully used UPM to reconstruct the whole speech signal, with enough naturality. 5 is achieved by a reconstruction from 12% of speech samples. t. the MMF, yet to be discovered in future studies. Acknowledgment. This work was funded by the INRIA CORDIS doctoral program.
I) can be expressed as: J L−1 x ˜i (t) = a ˜ijl s˜j (t − l) + n ˜ i (t), (1) j=1 l=0 where a ˜ijl represents the corresponding mixing ﬁlter coeﬃcient, s˜j (t) is the j-th source signal (j = 1, . . , J), n ˜ i (t) is the additive noise, and L is the length of the mixing ﬁlter. In the TF domain, the model (1) can be expressed as: J xif t = aijf sjf t + nif t , or equivalently X f = Af S f + N f , (2) j=1 where X f = [xif t ]f ∈ CI×T , Af = [aijf ]f ∈ CI×J , S f = [sjf t ]f ∈ CJ×T , N f = [nif t ]f ∈ CI×T , and f = 1, .