scholarly journals Variable learning rate EASI-based adaptive blind source separation in situation of nonstationary source and linear time-varying systems

2019 ◽  
Vol 21 (3) ◽  
pp. 627-638 ◽  
Author(s):  
Cheng Wang ◽  
Haiyang Huang ◽  
Yiwen Zhang ◽  
Yewang Chen
Keyword(s):  
Blind Source Separation ◽  
Linear Time ◽  
Source Separation ◽  
Learning Rate ◽  
Time Varying ◽  
Time Varying Systems ◽  
Nonstationary Source

A General Nonstationary and Time-Varying Mixed Signal Blind Source Separation Method Based on Online Gaussian Process

2020 ◽  
Vol 34 (11) ◽  
pp. 2058015
Author(s):  
Pengju He ◽  
Mi Qi ◽  
Wenhui Li ◽  
Mengyang Tang ◽  
Ziwei Zhao
Keyword(s):  
Gaussian Process ◽  
Blind Source Separation ◽  
Likelihood Function ◽  
Source Separation ◽  
Microphone Arrays ◽  
Time Varying ◽  
Posterior Distributions ◽  
Source Signal ◽  
Mixing Matrix ◽  
Nonstationary Source

Most nonstationary and time-varying mixed source separation algorithms are based on the model of instantaneous mixtures. However, the observation signal is a convolutional mixed source in reverberation environment, such as mobile voice received by indoor microphone arrays. In this paper, a time-varying convolution blind source separation (BSS) algorithm for nonstationary signals is proposed, which can separate both time-varying instantaneous mixtures and time-varying convolution mixtures. We employ the variational Bayesian (VB) inference method with Gaussian process (GP) prior for separating the nonstationary source frame by frame from the time-varying convolution signal, in which the prior information of the mixing matrix and the source signal are obtained by the Gaussian autoregressive method, and the posterior distributions of parameters (source signal and mixing matrix) are obtained by the VB learning. In the learning process, the learned parameters and hyperparameters are propagated to the next frame for VB inference as the prior which is combined with the likelihood function to get the posterior distribution. The experimental results show that the proposed algorithm is effective for separating time-varying mixed speech signals.

scholarly journals The VIT Transform Approach to Discrete-Time Signals and Linear Time-Varying Systems

Eng ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 99-125
Author(s):  
Edward W. Kamen
Keyword(s):  
Discrete Time ◽  
Initial Conditions ◽  
Linear Time ◽  
Order Term ◽  
Initial Time ◽  
Time Varying ◽  
Varying Coefficients ◽  
Time Signals ◽  
Time Varying Systems ◽  
Time Varying Coefficients

A transform approach based on a variable initial time (VIT) formulation is developed for discrete-time signals and linear time-varying discrete-time systems or digital filters. The VIT transform is a formal power series in z−1, which converts functions given by linear time-varying difference equations into left polynomial fractions with variable coefficients, and with initial conditions incorporated into the framework. It is shown that the transform satisfies a number of properties that are analogous to those of the ordinary z-transform, and that it is possible to do scaling of z−i by time functions, which results in left-fraction forms for the transform of a large class of functions including sinusoids with general time-varying amplitudes and frequencies. Using the extended right Euclidean algorithm in a skew polynomial ring with time-varying coefficients, it is shown that a sum of left polynomial fractions can be written as a single fraction, which results in linear time-varying recursions for the inverse transform of the combined fraction. The extraction of a first-order term from a given polynomial fraction is carried out in terms of the evaluation of zi at time functions. In the application to linear time-varying systems, it is proved that the VIT transform of the system output is equal to the product of the VIT transform of the input and the VIT transform of the unit-pulse response function. For systems given by a time-varying moving average or an autoregressive model, the transform framework is used to determine the steady-state output response resulting from various signal inputs such as the step and cosine functions.

scholarly journals Empirical Computation of Reachability Gramian for Linear-Time Varying Systems

2021 ◽  
Vol 54 (9) ◽  
pp. 119-124
Author(s):  
Kasturi Das ◽  
Srinivasan Krishnaswamy ◽  
Somanath Majhi
Keyword(s):  
Linear Time ◽  
Time Varying ◽  
Time Varying Systems
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