Bayesian nonstationary source separation

The detection of buried targets in shallow water is a tough task in the presence of sea-bottom reverberation. Because both target echo and reverberation are caused by the transmitted signal, they are mixed together in both time domain and frequency domain, which makes traditional signal processing methods inefficient. Blind Source Separation (BSS) is expected to isolate the reverberation from the target echo. However, the feasibility should be proved before separation. In this paper, a method based on spatial correlation is proposed to determine whether reverberation and target echo can be separated as different sources. Then, considering the nonstationarity of the reverberation, SONS (Second Order Nonstationary Source Separation) is applied to separate the original received signals. The sea experiment result shows that BSS is not only feasible but also valid to separate target echo and reverberation, and the target echo after BSS is of higher SRR which makes further process more credible.

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Natural gradient learning for second-order nonstationary source separation

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290) ◽

10.1109/ijcnn.2002.1005550 ◽

2003 ◽

Cited By ~ 1

Author(s):

Seungjin Choi ◽

A. Cichocki ◽

S. Amari

Keyword(s):

Source Separation ◽

Second Order ◽

Natural Gradient ◽

Gradient Learning ◽

Nonstationary Source

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

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800142058015x ◽

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.

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