Blind speech separation in time-domain using block-toeplitz structure of reconstructed signal matrices

2011 ◽  
Author(s):  
Zbyněk Koldovský ◽  
Jiří Málek ◽  
Petr Tichavský
Keyword(s):  
Time Domain ◽  
Speech Separation ◽  
Reconstructed Signal ◽  
Block Toeplitz Structure ◽  
Toeplitz Structure

scholarly journals Time Domain Audio Visual Speech Separation

2019 ◽  
Author(s):  
Jian Wu ◽  
Yong Xu ◽  
Shi-Xiong Zhang ◽  
Lian-Wu Chen ◽  
Meng Yu ◽  
...  
Keyword(s):  
Time Domain ◽  
Visual Speech ◽  
Speech Separation

scholarly journals Statistical View on Phase and Magnitude Information in Signal Processing

2012 ◽  
Vol 47 (3) ◽  
pp. 127-136
Author(s):  
Waldemar Popiński
Keyword(s):  
Signal Processing ◽  
Time Domain ◽  
Signal Reconstruction ◽  
Time Domain Signal ◽  
Reconstructed Signal ◽  
Band Pass ◽  
Expected Values ◽  
Random Fluctuations ◽  
Phase Distortions ◽  
Complex Valued

Statistical View on Phase and Magnitude Information in Signal ProcessingIn this work the problem of reconstruction of an original complex-valued signalot,t= 0, 1, …,n- 1, from its Discrete Fourier Transform (DFT) spectrum corrupted by random fluctuations of magnitude and/or phase is investigated. It is assumed that the magnitude and/or phase of discrete spectrum values are distorted by realizations of uncorrelated random variables. The obtained results of analysis of signal reconstruction from such distorted DFT spectra concern derivation of the expected values and bounds on variances of the reconstructed signal at the observation moments. It is shown that the considered random distortions in general entail change in magnitude and/or phase of the reconstructed signal expected values, which together with imposed random deviations with finite variances can blur the similarity to the original signal. The effect of analogous random amplitude and/or phase distortions of a complex valued time domain signal on band pass filtration of distorted signal is also investigated.

scholarly journals Diesel Engine Valve Clearance Fault Diagnosis Based on Improved Variational Mode Decomposition and Bispectrum

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 661 ◽  
Author(s):  
Xiaoyang Bi ◽  
Shuqian Cao ◽  
Daming Zhang
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Frequency Domain ◽  
Time Domain ◽  
Frequency Domain Analysis ◽  
Variational Mode Decomposition ◽  
Vibration Signals ◽  
Engine Valve ◽  
Mode Decomposition ◽  
Reconstructed Signal

The evaluation and fault diagnosis of a diesel engine’s health conditions without disassembly are very important for diesel engine safe operation. Currently, the research on fault diagnosis has focused on the time domain or frequency domain processing of vibration signals. However, early fault signals are mostly weak energy signals, and the fault information cannot be completely extracted by time domain and frequency domain analysis. Thus, in this article, a novel fault diagnosis method of diesel engine valve clearance using the improved variational mode decomposition (VMD) and bispectrum algorithm is proposed. First, the experimental study was designed to obtain fault vibration signals. The improved VMD method by choosing the optimal decomposition layers is applied to denoise vibration signals. Then the bispectrum analysis of the reconstructed signal after VMD decomposition is carried out. The results show that bispectrum image under different working conditions exhibits obviously different characteristics respectively. At last, the diagonal projection method proposed in this paper was used to process the bispectrum image, and the fourth order cumulant is calculated. The calculation results show that three states of the valve clearance are successfully distinguished.

scholarly journals Blind Speech Separation in Convolutive Mixtures Using Negentropy Maximization

2010 ◽  
Author(s):  
Vuong Hoang Nam ◽  
Nguyen Quoc Trung ◽  
Tran Hoai Linh
Keyword(s):  
Time Domain ◽  
Main Idea ◽  
New Method ◽  
Speech Separation ◽  
Simulation Experiments ◽  
Convolutive Mixtures ◽  
The Time Domain ◽  
Innovation Processes

This paper proposes a new method to address the  problem  of  blind  speech  separation  in  convolutive mixtures in the time domain. The main idea is extract the  innovation  processes  of  speech  sources  by  nonGaussianity  maximization  and  then  artificially  color them  by  re-coloration  filters.  Some  simulation experiments of the 2x2 case are presented to illustrate the proposed approach.

scholarly journals A Diagnosis Method for the Compound Fault of Gearboxes Based on Multi-Feature and BP-AdaBoost

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 461 ◽  
Author(s):  
Yangyang Zhang ◽  
Yunxian Jia ◽  
Weiyi Wu ◽  
Zhonghua Cheng ◽  
Xiaobo Su ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Failure Modes ◽  
Wavelet Packet ◽  
Rotating Machinery ◽  
Adaboost Algorithm ◽  
Feature Parameters ◽  
Reconstructed Signal ◽  
Diagnosis Method ◽  
Frequency Band Energy

Gearbox is an important structure of rotating machinery, and the accurate fault diagnosis of gearboxes is of great significance for ensuring efficient and safe operation of rotating machinery. Aiming at the problem that there is little common compound fault data of gearboxes, and there is a lack of an effective diagnosis method, a gearbox fault simulation experiment platform is set up, and a diagnosis method for the compound fault of gearboxes based on multi-feature and BP-AdaBoost is proposed. Firstly, the vibration signals of six typical states of gearbox are obtained, and the original signals are decomposed by empirical mode decomposition and reconstruct the new signal to achieve the purpose of noise reduction. Then, perform the time domain analysis and wavelet packet analysis on the reconstructed signal, extract three time domain feature parameters with higher sensitivity, and combine them with eight frequency band energy feature parameters obtained by wavelet packet decomposition to form the gearbox state feature vector. Finally, AdaBoost algorithm and BP neural network are used to build the BP-AdaBoost strong classifier model, and feature vectors are input into the model for training and verification. The results show that the proposed method can effectively identify the gearbox failure modes, and has higher accuracy than the traditional fault diagnosis methods, and has certain reference significance and engineering application value.

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