Matra

About: Matra is a based out in . It is known for research contribution in the topics: Signal & Combustion chamber. The organization has 1330 authors who have published 1216 publications receiving 17967 citations.

Biorthogonal bases of compactly supported wavelets

Abstract: Orthonormal bases of compactly supported wavelet bases correspond to subband coding schemes with exact reconstruction in which the analysis and synthesis filters coincide. We show here that under fairly general conditions, exact reconstruction schemes with synthesis filters different from the analysis filters give rise: to two dual Riesz bases of compactly supported wavelets. We give necessary and sufficient conditions for biorthogonality of the corresponding scaling functions, and we present a sufficient condition for the decay of their Fourier transforms. We study the regularity of these biorthogonal bases. We provide several families of examples, all symmetric (corresponding to “linear phase” filters). In particular we can construct symmetric biorthogonal wavelet bases with arbitrarily high preassigned regularity; we also show how to construct symmetric biorthogonal wavelet bases “close” to a (nonsymmetric) orthonormal basis.

Experiments with a Nonlinear Spectral Subtractor (NSS), Hidden Markov Models and the projection, for robust speech recognition in cars

Abstract: Achieving reliable performance for a speech recogniser is an important challenge, especially in the context of mobile telephony applications where the user can access telephone functions through voice The breakthrough of such a technology is appealing, since the driver can concentrate completely and safely on his task while composing and conversing in a “full” hands-free mode This paper addresses the problem of speaker-dependent discrete utterance recognition in noise Special reference is made to the mismatch effects due to the fact that training and testing are made in different environments A novel technique for noise compensation is proposed: nonlinear spectral subtraction (NSS) Robust variance estimates and robust pdf evaluations (projection) are also introduced and combined with NSS into the HMM framework We show that the lower limit of applicability of the projection (low SNR values) can be loosened after combination with NSS Experimental results are reported The performance of an HMM-based recogniser rises from 56% (no compensation) to 98% after speech enhancement More than 3300 utterances have been used to evaluate the systems (three databases, two European languages) This result is achieved by the use of robust training/recognition schemes and by preprocessing the noisy speech by NSS