Fault Detection and Localization using Wavelet Transform and Cross-correlation of Audio Signal

2014 ◽  
Vol 31 (4) ◽  
pp. 327-334 ◽  
Author(s):  
Hyo Geun Ji ◽  
Jung Hyun Kim
Keyword(s):  
Wavelet Transform ◽  
Fault Detection ◽  
Cross Correlation ◽  
Audio Signal ◽  
Detection And Localization

scholarly journals Modified GSC Method to Reduce the Distortion of the Enhanced Speech Signal Using Cross-Correlation and Sidelobe Neutralization

2021 ◽  
Vol 11 (14) ◽  
pp. 6288
Author(s):  
Hang Su ◽  
Chang-Myung Lee
Keyword(s):  
Speech Signal ◽  
Output Signal ◽  
Cross Correlation ◽  
Acoustic Noise ◽  
Audio Signal ◽  
Noise Signal ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Experiment Data ◽  
Noise Component

The generalized sidelobe canceller (GSC) method is a common algorithm to enhance audio signals using a microphone array. Distortion of the enhanced audio signal consists of two parts: the residual acoustic noise and the distortion of the desired audio signal, which means that the desired audio signal is damaged. This paper proposes a modified GSC method to reduce both kinds of distortion when the desired audio signal is a non-stationary speech signal. First, the cross-correlation coefficient between the canceling signal and the error signal of the least mean square (LMS) algorithm was added to the adaptive process of the GSC method to reduce the distortion of the enhanced signal while the energy of the desired signal frame was increased suddenly. The sidelobe pattern of beamforming was then presented to estimate the noise signal in the beamforming output signal of the GSC method. The noise component of the beamforming output signal was decreased by subtracting the estimated noise signal to improve the denoising performance of the GSC method. Finally, the GSC-SN-MCC method was proposed by merging the above two methods. The experiment was performed in an anechoic chamber to validate the proposed method in various SNR conditions. Furthermore, the simulated calculation with inaccurate noise directions was conducted based on the experiment data to inspect the robustness of the proposed method to the error of the estimated noise direction. The experiment data and calculation results indicated that the proposed method could reduce the distortion effectively under various SNR conditions and would not cause more distortion if the estimated noise direction is far from the actual noise direction.

scholarly journals Detection and Localization for Multiple Stationary Human Targets Based on Cross-Correlation of Dual-Station SFCW Radars

2019 ◽  
Vol 11 (12) ◽  
pp. 1428 ◽  
Author(s):  
Yong Jia ◽  
Yong Guo ◽  
Chao Yan ◽  
Haoxuan Sheng ◽  
Guolong Cui ◽  
...  
Keyword(s):  
Cross Correlation ◽  
Continuous Wave ◽  
Coefficient Matrix ◽  
Constant False Alarm Rate ◽  
Localization Algorithm ◽  
Cfar Detection ◽  
Stepped Frequency ◽  
The Cross ◽  
The One ◽  
Detection And Localization

This paper demonstrates the feasibility of detection and localization of multiple stationary human targets based on cross-correlation of the dual-station stepped-frequency continuous-wave (SFCW) radars. Firstly, a cross-correlation operation is performed on the preprocessed pulse signals of two SFCW radars at different locations to obtain the correlation coefficient matrix. Then, the constant false alarm rate (CFAR) detection is applied to extract the ranges between each target and the two radars, respectively, from the correlation matrix. Finally, the locations of human targets is calculated with the triangulation localization algorithm. This cross-correlation operation mainly brings about two advantages. On the one hand, the cross-correlation explores the correlation feature of target respiratory signals, which can effectively detect all targets with different signal intensities, avoiding the missed detection of weak targets. On the other hand, the pairing of two ranges between each target and two radars is implemented simultaneously with the cross-correlation. Experimental results verify the effectiveness of this algorithm.

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