Abstract There is a growing interest in probabilistic numerical solutions to ordinary differential equations. In this paper, the maximum posteriori estimate studied under class of $$\nu $$ ν times differentiable linear time-invariant Gauss–Markov priors, which can be computed with an iterated extended Kalman ...

Multipath arrivals of a signal at a receiving sensor are frequently encountered in many areas of signal processing, including sonar, radar, and communication problems. In underwater acoustics, numerous approaches to source localization, geoacoustic inversion, and tomography rely on accurate multipath arrival extraction. In this work, a novel method for estimation of time delays and amplitudes o...

This paper proposes a Bayesian affine transformation of hidden Markov model (HMM) parameters for reducing the acoustic mismatch problem in telephone speech recognition. Our purpose is to transform the existing HMM parameters into its new version of specific telephone environment using affine function so as to improve the recognition rate. The maximum a posteriori (MAP) estimation which merges t...

Eigenvoice is an effective speaker adaptation approach and capable of balancing the performance and the requirement for a large amount of adaptation data. However, the conventional Maximum Likelihood Eigen-Decomposition (MLED) method in eigenvoice adaptation is based on Maximum Likelihood (ML) criterion and suffers from the unrealistic assumption made by HMM on speech process, so alternative sc...

In this paper, the main ideas of a new method for multi-microphone speech enhancement are presented. Combining previously known multichannel speech enhancement methods with Maximum A Posteriori (MAP) estimation concepts, and temporally whitenning the output noises, we obtain a new algorithm called Adaptive Time-domain Signal Estimation for MultiMicrophone (ATSEM). The simulations (using real-wo...