Christine Servière

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This paper presents a method for blind separation of convolutive mixtures of speech signals, based on the joint diagonalization of the time varying spectral matrices of the observation records and a novel technique to handle the problem of permutation ambiguity in the frequency domain. Simulations show that our method works well even for rather realistic(More)
In this paper we present a new model-based blind speech separation for underdetermined case. Under sparsity assumption, separation is achieved by applying soft time frequency masks to observations. The masks are derived by estimating the parameters of an ad-hoc distribution of the Interchannel Level/Phase Difference (ILD/IPD). These parameters are estimated(More)
— Recently, several three-axial MEMS-based force sensors have been developed. This kind of force micro sensor is also called tactile sensor in literature for its similarities in size and sensitivity with human mechanoreceptors. Therefore, we believe these three-axial force sensors being able to analyse textures properties while sliding on a surface, as(More)
There is a great interest to apply BSS methods in mechanical system signal processing for monitoring or diagnosis purpose. Actually, we show that BSS allows to recover the vibratory information issued from a single rotating machine working in a noisy environment by freeing the sensor signal from the contribution of other working machines. In that way, BSS(More)
This paper demonstrates the utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary. The technique was evaluated by three stationarity tests consisting of the variation of two statistical properties, i.e., mean and standard deviation, and the reverse(More)