Jean-Marc Brossier

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In this paper, we present a closed-form expression of a Bayesian Cramér-Rao lower bound for the estimation of a dynamical phase offset in a non-data-aided BPSK transmitting context. This kind of bound is derived considering two different scenarios: a first expression is obtained in an off-line context and then, a second expression in an on-line context(More)
—In this paper, the issue of audio source separation from a single channel is addressed, i.e. the estimation of several source signals from a single observation of their mixture. This challenging problem is tackled with a specific two levels coder-decoder configuration. At the coder, source signals are assumed to be available before the mix is processed.(More)
—This letter deals with the Cramér–Rao bound for the estimation of a hybrid vector with both random and deterministic parameters. We point out the specificity of the case when the deter-ministic and the random vectors of parameters are statistically dependent. The relevance of this expression is illustrated by studying a practical phase estimation problem(More)
In this paper, we address the issue of audio source separation with a single channel, i.e. the estimation of source signals from a single mixture of these signals. This problem is addressed with a specific configuration: source signals are assumed to be available before the mix is processed. We propose an original method that uses a wa-termarking technique(More)
This letter deals with the estimation of a flat fading Rayleigh channel with Jakes's spectrum. The channel is approximated by a first-order autoregressive (AR(1)) model and tracked by a Kalman Filter (KF). The common method used in the literature to estimate the parameter of the AR(1) model is based on a Correlation Matching (CM) criterion. However, for(More)
This paper deals with the on-line carrier phase estimation in a digital receiver. We consider a Brownian phase evolution in a Data Aided scenario. The proposed study uses an oversampled signal model after matched filtering, leading to a coloured reception noise and a non-stationary power signal. The contribution of this paper is twofold. First, we derive(More)
—In this paper, adaptive estimation based on noisy quantized observations is studied. A low complexity adaptive algorithm using a quantizer with adjustable input gain and offset is presented. Three possible scalar models for the parameter to be estimated are considered: constant, Wiener process and Wiener process with deterministic drift. After showing that(More)