An Impulse Noise Robust Noise Estimation Algorithm Applied for Low Signal-to-Noise Ratio Digital Communication

2006 ◽  
Author(s):  
Tong Wang ◽  
Hui-juan Cui ◽  
Kun Tang
Keyword(s):  
Impulse Noise ◽  
Digital Communication ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Noise Estimation ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Noise Robust

scholarly journals Improved signal-to-noise ratio estimation algorithm for asymmetric pulse-shaped signals

2015 ◽  
Vol 9 (14) ◽  
pp. 1788-1792 ◽  
Author(s):  
Huaizong Shao ◽  
Wuling Liu ◽  
Di Wu ◽  
Xiaoli Chu ◽  
Yang Li
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Ratio Estimation ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Asymmetric Pulse

scholarly journals A Low-Signal-to-Noise Ratio Estimation Algorithm for Multipath Channels

2020 ◽  
Vol 25 (2) ◽  
pp. 253-258
Author(s):  
Baohai Yang ◽  
Quanhui Ren ◽  
Haisheng Li ◽  
Junkang Song
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Multipath Channels ◽  
Ratio Estimation ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Signal-to-Noise Ratio Estimation Algorithm for Advanced DVB-RCS Systems

2012 ◽  
Vol 58 (4) ◽  
pp. 603-608 ◽  
Author(s):  
Ayesha Ijaz ◽  
Adegbenga B. Awoseyila ◽  
Barry G. Evans
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Ratio Estimation ◽  
Signal To Noise ◽  
Noise Ratio

scholarly journals Reduction of Impulsive Noise from Speech and Audio Signals by using Sd-Rom Algorithm

2021 ◽  
Vol 10 (1) ◽  
pp. 265-268
Author(s):  
G.Manmadha Rao* ◽  
Raidu Babu D.N ◽  
Krishna Kanth P.S.L ◽  
Vinay B. ◽  
Nikhil V.
Keyword(s):  
Impulse Noise ◽  
Impulsive Noise ◽  
Rank Order ◽  
Signal To Noise Ratio ◽  
Audio Signal ◽  
Window Size ◽  
Mean Value ◽  
Audio Signals ◽  
Signal To Noise ◽  
Noise Ratio

Removal of noise is the heart for speech and audio signal processing. Impulse noise is one of the most important noise which corrupts different parts in speech and audio signals. To remove this type of noise from speech and audio signals the technique proposed in this work is signal dependent rank order mean (SD-ROM) method in recursive version. This technique is used to replace the impulse noise samples based on the neighbouring samples. It detects the impulse noise samples based on the rank ordered differences with threshold values. This technique doesn’t change the features and tonal quality of signal. Rank ordered differences is used for detecting the impulse noise samples in speech and audio signals. Once the sample is detected as corrupted sample, that sample is replaced with rank ordered mean value and this rank ordered mean value depends on the sliding window size and neighbouring samples. This technique shows good results in terms of signal to noise ratio (SNR) and peak signal to noise ratio (PSNR) when compared with other techniques. It mainly used for removal of impulse noises from speech and audio signals.

scholarly journals Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiaojuan Xie ◽  
Shengliang Peng ◽  
Xi Yang
Keyword(s):  
Deep Learning ◽  
Data Transmission ◽  
Signal To Noise Ratio ◽  
Estimation Algorithm ◽  
Spectrum Management ◽  
Signal To Noise ◽  
Estimation Errors ◽  
Snr Estimation ◽  
Low Snr ◽  
Noise Ratio

Signal-to-noise ratio (SNR) estimation is a fundamental task of spectrum management and data transmission. Existing methods for SNR estimation usually suffer from significant estimation errors when SNR is low. This paper proposes a deep learning (DL) based SNR estimation algorithm using constellation diagrams. Since the constellation diagrams exhibit different patterns at different SNRs, the proposed algorithm achieves SNR estimation via constellation diagram recognition, which can be easily handled based on DL. Three DL networks, AlexNet, InceptionV1, and VGG16, are utilized for DL based SNR estimation. Experimental results show that the proposed algorithm always performs well, especially in low SNR scenarios.

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