Marcin Kuropatwinski

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Speech coding algorithms that have been developed for clean speech are often used in a noisy environment. We describe maximum a posteriori (MAP) and minimum mean square error (MMSE) techniques to estimate the clean-speech short-term predictor (STP) parameters from noisy speech. The MAP and MMSE estimates are obtained using a likelihood function computed by(More)
We consider adaptive two-channel multiple-description coding. We provide an analytical method for designing a resolution- constrained symmetrical multiple-description coder that uses an index assignment matrix. We use existing index assignment algorithms that are known for their good properties within our adaptive multiple-description coding architecture.(More)
It is shown that robust dimension-reduction of a feature set for speech recognition can be based on a model of the human auditory system. Whereas conventional methods optimize classification performance, the proposed method exploits knowledge implicit in the auditory periphery, inheriting its robustness. Features are selected to maximize the similarity of(More)
Speech coding techniques commonly used in low bit rate analysis-by-synthesis linear predictive coders (LPAS coders) create a model that emphasizes the important features of a speech signal. The utilization of these coding methods for speech enhancement is shown. Specifically, the speech signal will be modeled as the output of a cascade of an adaptive(More)
In this paper, the estimation of speech AR parameters under noisy conditions is revisited. The EM algorithm serving this purpose was first proposed by Gannot et al. We present an extensive experimental study along with a new approach to implement the E-step of the algorithm. The new realization of the E-step uses matrix computations instead of a Kalman(More)
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