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Unknown global permutation of the separated sources, time-varying source activity and under determination are common problems affecting on-line Independent Vector Analysis when applied to real-world speech enhancement. In this work we propose to extend the signal model of IVA by introducing additional supervising components. Pilot signals, which are dependent on the sources, are injected in the multidimensional...
To adapt to the sparsity of some sparse systems in system identification, a novel modified recursive least squares algorithm is proposed. This algorithm utilizes the output error to control the value of forgetting factor, which deals with the contradiction between convergence rate and stationary misadjustment. In addition, through the introduction of zero attractor in parameters' iterations, the proposed...
A more efficient pre-initializing strategy of PSO algorithm: Multi-Period Particle Swarm Optimization (MP-PSO) is proposed. The process is divided into two periods: pre-initialization and post-optimization. The former is determined to find a better local solution to initialize the next period instead of standard uniform randomness. In order to explore further, adaptive escaping weight is adopted to...
This paper proposes a new particle swarm optimization (PSO) algorithm with an adaptive weight. Benchmark tests of the algorithm is described. Compared with standard PSO, it shows better convergence as well as ability of escaping from local optima. Diesel engines must meet the increasing demands for higher efficiency, cleaner exhaust gases and better drivability. Model-Based control is one of effective...
Gaussian mixture models have been extensively used and enhanced in the surveillance domain because of their ability to adaptively describe multimodal distributions in real-time with low memory requirements. Nevertheless, they still often suffer from the problem of converging to poor solutions if the main mode stretches and thus over-dominates weaker distributions. Based on the results of the Split...
In this work a low-complexity strategy for accelerating the convergence of convex combinations of adaptive filters is proposed. The idea is based on an instantaneous transfer of coefficients from a fast adaptive filter to a slow adaptive filter, which is performed according to a pre-defined window length. A theoretical model that is capable of predicting the excess mean squared error (EMSE) of the...
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