scholarly journals Comparison of LMS and NLMS algorithm with the using of 4 Linear Microphone Array for Speech Enhancement.

2017 ◽  
Vol 2 (4) ◽  
pp. 15
Author(s):  
Mamun Ahmed ◽  
Nasimul Hyder Maruf Bhuyan
Keyword(s):  
Speech Enhancement ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Microphone Array ◽  
Side Lobe ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Noise Sources ◽  
Whole Process ◽  
Nlms Algorithm

In this paper, we have presented the design, implementation and comparison result of Least Mean Square (LMS) algorithm and Normalized LMS (NLMS) algorithm using a 4 channel microphone array for noise reduction as well as speech enhancement. Adaptive sub band Generalized Side lobe Canceller (GSC) beam former has been used for experiment and analysis. Tested results were done by using one speech signal and a small number of noise sources. The side lobe canceller was evaluated with the adaptation of LMS and NLMS. The overall development of Signal to Noise Ratio (SNR) has been determined from the input and output powers of signal and noise, with signal only as input and noise, as input to the GSC. The NLMS algorithm considerably improves speech quality with noise suppression levels of up to 13 dB, while the LMS algorithm is giving up to 10 dB. In different ways of SNR measure was under various types of blocking matrix, step sizes and various noise locations. The whole process will be used for hands-free telephony, video conferencing etc. in a noisy environment.

scholarly journals Comparative Study on Noise Reduction Effect of Fiber Optic Hydrophone Based on LMS and NLMS Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 301
Author(s):  
Zhihua Yu ◽  
Yunfei Cai ◽  
Daili Mo
Keyword(s):  
Adaptive Filtering ◽  
Fiber Optic ◽  
Signal To Noise Ratio ◽  
Harmonic Distortion ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Real Time Processing ◽  
Nlms Algorithm ◽  
Filtering Effect

Adaptive filtering has the advantages of real-time processing, small computational complexity, and good adaptability and robustness. It has been widely used in communication, navigation, signal processing, optical fiber sensing, and other fields. In this paper, by adding an interferometer with the same parameters as the signal interferometer as the reference channel, the sensing signal of the interferometric fiber-optic hydrophone is denoised by two adaptive filtering schemes based on the least mean square (LMS) algorithm and the normalized least mean square (NLMS) algorithm respectively. The results show that the LMS algorithm is superior to the NLMS algorithm in reducing total harmonic distortion, improving the signal-to-noise ratio and filtering effect.

scholarly journals Sign Regressor based Normalized Adaptive Filters for Speech Enhancement Applications

2018 ◽  
Vol 7 (2.17) ◽  
pp. 79
Author(s):  
Jyoshna Girika ◽  
Md Zia Ur Rahman
Keyword(s):  
Steady State ◽  
Speech Enhancement ◽  
Adaptive Filters ◽  
Signal To Noise Ratio ◽  
Speech Signals ◽  
Mean Square ◽  
Least Mean Square ◽  
Union Rate ◽  
Step Size ◽  
Adaptive Noise

Removal of noise components of speech signals in mobile applications  is an important step to facilitate high resolution signals to the user. Throughout the communication method the speech signals are tainted by numerous non stationary noises. The Least Mean Square (LMS) technique is a fundamental adaptive technique usedbroadly in numerouspurposes as anoutcome of its plainness as well as toughness. In LMS technique, an importantfactor is the step size. It bewell-known that if the union rate of the LMS technique will be rapidif the step size is speedy, but the steady-state mean square error (MSE) will raise. On the rival, for the diminutive step size, the steady state MSE will be minute, but the union rate will be conscious. Thus, the step size offers anexchange among the convergence rate and the steady-state MSE of the LMS technique. Build the step size variable before fixed to recover the act of the LMS technique, explicitly, prefer large step size values at the time of the earlyunion of the LMS technique, and usetiny step size values when the structure is near up to its steady state, which results in Normalized LMS (NLMS) algorithms. In this practice the step size is not stable and changes along with the fault signal at that time. The Less mathematical difficulty of the adaptive filter is extremely attractive in speech enhancement purposes. This drop usually accessible by extract either the input data or evaluation fault.  The algorithms depend on an extract of fault or data are Sign Regressor (SR) Algorithms. We merge these sign version to various adaptive noise cancellers. SR Weight NLMS (SRWNLMS), SR Error NLMS (SRENLMS), SR Unbiased LMS (SRUBLMS) algorithms are individual introduced as a quality factor. These Adaptive noise cancellers are compared with esteem to Signal to Noise Ratio Improvement (SNRI). 

Extracting symptoms of bearing faults from noise using a non-linear neural filter

2002 ◽  
Vol 216 (2) ◽  
pp. 169-179 ◽  
Author(s):  
Y Shao ◽  
K Nezu
Keyword(s):  
Adaptive Filter ◽  
Signal To Noise Ratio ◽  
Effective Means ◽  
Failure Detection ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Signal To Noise ◽  
Non Linear ◽  
Detection Capabilities ◽  
Noise Ratio

Improving the signal-to-noise ratio is an important feature for the early detection of faults in bearings subject to large amounts of environmental noises. A method is proposed for improving the signal-to-noise ratio by adaptive neural filtering (ANF). A comparison of failure detection capabilities of a linear adaptive filter using the least mean square (LMS) algorithm and a non-linear adaptive filter using the ANF algorithm in conditions of large amounts of environmental noise is made. Experimental results show that an adaptive filter using a neural filtering algorithm is an effective means for extracting the symptoms of a bearing fault under such conditions.

scholarly journals Improvement of LMS adaptive noise canceller using uniform Poly-phase digital filter bank

2020 ◽  
Vol 17 (3) ◽  
pp. 1258
Author(s):  
Alaa Hadi Mohammad ◽  
Azura Che Soh ◽  
Noor Faezah Ismail ◽  
Ribhan Zafira Abdul Rahman ◽  
Mohd Amran Mohd Radzi
Keyword(s):  
Digital Filter ◽  
Filter Bank ◽  
Signal To Noise Ratio ◽  
Process Analysis ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Full Band ◽  
Synthesis Filter ◽  
Adaptive Noise Canceller ◽  
Digital Filter Bank

<span>This paper presents the Least Mean Square (LMS) noise canceller using uniform poly-phase digital filter bank to improve the noise can-cellation process. Analysis filter bank is used to decompose the full-band distorted input signal into sub-band signals. Decomposition the full-band input distorted signal into sub-band signals based on the fact that the signal to noise ratio (S/N) is inversely proportional to the signal bandwidth. Each sub-band signal is fed to individual LMS algorithm to produce the optimal sub-band output. Synthesis filter bank is used to compose the optimal sub-band outputs to produce the final optimal full-band output. In this paper, m-band uniform Discrete Fourier Transform (DFT) digital filter bank has been used because its computational complexity is much smaller than the direct implementation of digital filter bank. The simulation results show that the proposed method provides the efficient performance with less and smooth error signal as compared to conventional LMS noise canceller.</span>

A Study of a various Acoustic Beamforming Techniques Using a Microphone Array

2015 ◽  
Vol 1 ◽  
pp. 7 ◽  
Author(s):  
Ahmed Abdalla ◽  
Suhad Mohammed ◽  
Abdelazeim Abdalla ◽  
Tang Bin ◽  
Mohammed Ramadan
Keyword(s):  
Signal To Noise Ratio ◽  
Microphone Array ◽  
Spatial Filtering ◽  
Mean Square ◽  
Least Mean Square ◽  
Signal To Noise ◽  
Least Mean Squares ◽  
Lms Filter ◽  
Least Mean Square Algorithm ◽  
Simulation Results

In this paper, A study of numerous acoustic beamforming algorithms is carried out. Beamforming algorithms are techniques utilize to determine the Direction of arrival of (DOA) the speech signals while suppress out the corresponding noises and interferences. The simple delay and sum beamformer technique which use the constrained least mean squares (LMS) filter for spatial filtering is firstly investigated. Secondly, a constrained least mean square algorithm (also known as Frost Beamformer) is considered. The beamformer algorithms are simulated in MATLAB and therefore, the simulation results indicate that there a significant enhancement in the Signal-to-Noise-Ratio (SNR) for frost beamformer as compared to the simple delay and sum beamformer.

Generalized Sidelobe Canceller Beamforming with Combined Postfilter and Sparse NMF for Speech Enhancement

2020 ◽  
pp. 2150014
Author(s):  
S. Siva Priyanka ◽  
T. Kishore Kumar
Keyword(s):  
Speech Enhancement ◽  
Matrix Factorization ◽  
Microphone Array ◽  
Least Mean Square ◽  
Generalized Sidelobe Canceller ◽  
Residual Noise ◽  
Enhancement Method ◽  
Sidelobe Canceller ◽  
Diffuse Noise ◽  
Non Negative Matrix Factorization

A multi-microphone array speech enhancement method using Generalized Sidelobe Canceller (GSC) beamforming with Combined Postfilter (CP) and Sparse Non-negative Matrix Factorization (SNMF) is proposed in this paper. GSC beamforming with CP and SNMF is implemented to reduce directional noise, diffuse noise, residual noise and to separate interferences in adverse environment. In this paper, the directional noise is reduced using GSC beamforming, whereas the diffuse noise in each subband is reduced with a combined postfilter using Unconstrained Frequency domain Normalized Least Mean Square (UFNLMS) algorithm. Finally, the residual noise at the output of CP is eliminated by SNMF which optimizes the noise. The performance of the proposed method is evaluated using parameters like PESQ, SSNR, STOI, SDR and LSD. The noise reduction for four and eight microphones is compared and illustrated in spectrograms. The proposed method shows better performance in terms of intelligibility and quality when compared to the existing methods in adverse environments.

scholarly journals Speech Enhancement Based on the Multi-Scales and Multi-Thresholds of the Auditory Perception Wavelet Transform

2011 ◽  
Vol 36 (3) ◽  
pp. 519-532 ◽  
Author(s):  
Zhi Tao ◽  
He-Ming Zhao ◽  
Xiao-Jun Zhang ◽  
Di Wu
Keyword(s):  
Wavelet Transform ◽  
Speech Enhancement ◽  
Auditory Perception ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Masking Effect ◽  
Auditory Masking ◽  
Low Snr ◽  
Residual Noise ◽  
Multi Scales

Abstract This paper proposes a speech enhancement method using the multi-scales and multi-thresholds of the auditory perception wavelet transform, which is suitable for a low SNR (signal to noise ratio) environment. This method achieves the goal of noise reduction according to the threshold processing of the human ear's auditory masking effect on the auditory perception wavelet transform parameters of a speech signal. At the same time, in order to prevent high frequency loss during the process of noise suppression, we first make a voicing decision based on the speech signals. Afterwards, we process the unvoiced sound segment and the voiced sound segment according to the different thresholds and different judgments. Lastly, we perform objective and subjective tests on the enhanced speech. The results show that, compared to other spectral subtractions, our method keeps the components of unvoiced sound intact, while it suppresses the residual noise and the background noise. Thus, the enhanced speech has better clarity and intelligibility.

scholarly journals Gearbox Fault Features Extraction Using Vibration Measurements and Novel Adaptive Filtering Scheme

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Ghalib R. Ibrahim ◽  
A. Albarbar
Keyword(s):  
Adaptive Filtering ◽  
Signal To Noise Ratio ◽  
Substantial Improvement ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Step Size ◽  
Total Load ◽  
Fault Features ◽  
Multicomponent Signals ◽  
Tooth Removal

Vibration signals measured from a gearbox are complex multicomponent signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures, and a substantial amount of noise. This paper presents a novel scheme for extracting gearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A modified least mean square (LMS) algorithm is examined and validated using only one accelerometer, instead of using two accelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured shaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with a numerous values of step size. Findings confirm that 10−5 step size invariably produces more accurate results and there has been a substantial improvement in signal clarity (better signal-to-noise ratio), which makes meshing frequency sidebands more discernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a healthy pair of gears and a pair suffering from a tooth breakage with severity fault 1 (25% tooth removal) and fault 2 (50% tooth removal) under loads (0%, and 80% of the total load). The experimental results show remarkable improvements and enhance gear condition features. This paper illustrates that the new approach offers a more effective way to detect early faults.

scholarly journals Low-complexity artificial noise suppression methods for deep learning-based speech enhancement algorithms

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yuxuan Ke ◽  
Andong Li ◽  
Chengshi Zheng ◽  
Renhua Peng ◽  
Xiaodong Li
Keyword(s):  
Deep Learning ◽  
Speech Enhancement ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Low Complexity ◽  
Speech Quality ◽  
Artificial Noise ◽  
Noise Power ◽  
Noise Masking ◽  
Residual Noise

AbstractDeep learning-based speech enhancement algorithms have shown their powerful ability in removing both stationary and non-stationary noise components from noisy speech observations. But they often introduce artificial residual noise, especially when the training target does not contain the phase information, e.g., ideal ratio mask, or the clean speech magnitude and its variations. It is well-known that once the power of the residual noise components exceeds the noise masking threshold of the human auditory system, the perceptual speech quality may degrade. One intuitive way is to further suppress the residual noise components by a postprocessing scheme. However, the highly non-stationary nature of this kind of residual noise makes the noise power spectral density (PSD) estimation a challenging problem. To solve this problem, the paper proposes three strategies to estimate the noise PSD frame by frame, and then the residual noise can be removed effectively by applying a gain function based on the decision-directed approach. The objective measurement results show that the proposed postfiltering strategies outperform the conventional postfilter in terms of segmental signal-to-noise ratio (SNR) as well as speech quality improvement. Moreover, the AB subjective listening test shows that the preference percentages of the proposed strategies are over 60%.

scholarly journals A Novel Modulation Classification Approach Using Gabor Filter Network

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Sajjad Ahmed Ghauri ◽  
Ijaz Mansoor Qureshi ◽  
Tanveer Ahmed Cheema ◽  
Aqdas Naveed Malik
Keyword(s):  
Feature Extraction ◽  
Gabor Filter ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Lms Algorithm ◽  
Modulation Classification ◽  
Least Mean Square ◽  
Delta Rule ◽  
Modulated Signals

A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel.

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