BLIND SOURCE SEPARATION OF AUDIO SIGNALS USING WVD-KR ALGORITHM

Separation of MECG and FECG using CCA-EMD Process

International Journal of Electronics Signals and Systems ◽

10.47893/ijess.2014.1172 ◽

2014 ◽

pp. 218-222

Author(s):

D. SUGUMAR ◽

NEETHU SUSAN RAJAN ◽

P. T. VANATHI

Keyword(s):

Blind Source Separation ◽

Source Separation ◽

Improved Method ◽

Time Frequency ◽

Mixing Matrix ◽

Simulation Results ◽

Stationary Sources

Under-determined blind source separation aims to separate N non-stationary sources from M (M<N) mixtures.Paper presents a time-frequency approach (TF) to under-determined blind source separation of N non-stationary sources from M mixtures(M<N). It is based on Wigner-Ville distribution and Khatri-Rao product. Improved method involves a two step approach which involves the estimation of the mixing matrix where negative values of auto WVD of the sources are fully considered and secondly auto-term TF points are extracted.After extracting the auto-term TF points source WVD values at every TF point are computed using a new algorithm based on Khatri-Rao product. Thus sources are separated with the proposed approach no matter how many active sources there are as long as N≤ 2M-1.Simulation results are presented to show the superiority of the proposed algorithm by comparing it with the existing algorithms.

Download Full-text

A Novel Underdetermined Blind Source Separation Method Based on OPTICS and Subspace Projection

Symmetry ◽

10.3390/sym13091677 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1677

Author(s):

Qingyi Wang ◽

Yiqiong Zhang ◽

Shuai Yin ◽

Yuduo Wang ◽

Genping Wu

Keyword(s):

Blind Source Separation ◽

Source Separation ◽

Estimation Method ◽

Signal Recovery ◽

Subspace Projection ◽

Underdetermined Blind Source Separation ◽

Matrix Estimation ◽

Time Frequency ◽

Source Signal ◽

Mixing Matrix

In recent years, the problem of underdetermined blind source separation (UBSS) has become a research hotspot due to its practical potential. This paper presents a novel method to solve the problem of UBSS, which mainly includes the following three steps: Single source points (SSPs) are first screened out using the principal component analysis (PCA) approach, which is based on the statistical features of signal time-frequency (TF) points. Second, a mixing matrix estimation method is proposed that combines Ordering Points To Identify the Clustering Structure (OPTICS) with an improved potential function to directly detect the number of source signals, remove noise points, and accurately calculate the mixing matrix vector; it is independent of the input parameters and offers great accuracy and robustness. Finally, an improved subspace projection method is used for source signal recovery, and the upper limit for the number of active sources at each mixed signal is increased from m−1 to m. The unmixing process of the proposed algorithm is symmetrical to the actual signal mixing process, allowing it to accurately estimate the mixing matrix and perform well in noisy environments. When compared to previous methods, the source signal recovery accuracy is improved. The method’s effectiveness is demonstrated by both theoretical and experimental results.

Download Full-text

A Novel Blind Source Separation Algorithm for Linear Mixture Signals

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1357 ◽

2014 ◽

Vol 599-601 ◽

pp. 1357-1359

Author(s):

Wei Hua Liu ◽

Yun Zhang ◽

Ying Fu Chen ◽

Lei Wang ◽

Jian Cheng Liu

Keyword(s):

Cost Function ◽

Blind Source Separation ◽

Source Separation ◽

Separation Algorithm ◽

Mixing Matrix ◽

Separate Source ◽

Simulation Results ◽

The Cost ◽

Source Signals

A novel blind source separation (BSS) algorithm for linear mixture signals is proposed. It is shown that the property can be used to separate source signals by finding an un-mixing matrix that maximizes the cost function value of separated signals. Simulation results illustrate the efficiency and the good performance of the algorithm.

Download Full-text

Linear Multichannel Blind Source Separation based on Time-Frequency Mask Obtained by Harmonic/Percussive Sound Separation

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp39728.2021.9413494 ◽

2021 ◽

Author(s):

Soichiro Oyabu ◽

Daichi Kitamura ◽

Kohei Yatabe

Keyword(s):

Blind Source Separation ◽

Source Separation ◽

Time Frequency ◽

Sound Separation

Download Full-text

An Underdetermined Blind Source Separation Method with Application to Modal Identification

Shock and Vibration ◽

10.1155/2019/1637163 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Gang Yu

Keyword(s):

Blind Source Separation ◽

Source Separation ◽

Modal Identification ◽

Modal Parameters ◽

Underdetermined Blind Source Separation ◽

Structural Dynamic ◽

Time Frequency ◽

Modal Response ◽

Experimental Validations ◽

Straight Lines

In structural dynamic analysis, the blind source separation (BSS) technique has been accepted as one of the most effective ways for modal identification, in which how to extract the modal parameters using very limited sensors is a highly challenging task in this field. In this paper, we first review the drawbacks of the conventional BSS methods and then propose a novel underdetermined BSS method for addressing the modal identification with limited sensors. The proposed method is established on the clustering features of time-frequency (TF) transform of modal response signals. This study finds that the TF energy belonging to different monotone modals can cluster into distinct straight lines. Meanwhile, we provide the detailed theorem to explain the clustering features. Moreover, the TF coefficients of each modal are employed to reconstruct all monotone signals, which can benefit to individually identify the modal parameters. In experimental validations, two experimental validations demonstrate the effectiveness of the proposed method.

Download Full-text