scholarly journals A Noise Reduction Method Based on Modified LMS Algorithm of Real time Speech Signals

2021 ◽  
Vol 16 ◽  
pp. 162-170
Author(s):  
Jagadish S. Jakati ◽  
Shridhar S. Kuntoji
Keyword(s):  
Real Time ◽  
Adaptive Filtering ◽  
Speech Signal ◽  
Reduction Method ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Noise Characteristics ◽  
Filtering Technique ◽  
Least Mean Square Algorithm ◽  
Proof Of Convergence

In real time speech de-noising, adaptive filtering technique with variable length filters are used which is used to track the noise characteristics and through those characteristics the filter equations are selected The main features that attracted the use of the LMS algorithm are low computational complexity, proof of convergence in stationary environment. In this paper, modified LMS algorithm is proposed which is used to denoise real time speech signal. The proposed algorithm is made by combining general LMS algorithm with Diffusion least mean-square algorithm which increase the capabilities of adaptive filtering. The performance parameter calculation shows that the proposed algorithm is effective to de-noise speech signal. A full programming routine written in MATLAB software is provided for replications and further research applications.

scholarly journals Adaptive Noise Cancellation Techniques for Impedance Cardiography Signal Analysis

2019 ◽  
Vol 8 (9) ◽  
pp. 122-130
Keyword(s):  
Real Time ◽  
Adaptive Filtering ◽  
Impedance Cardiography ◽  
Signal Enhancement ◽  
The Body ◽  
Lms Algorithm ◽  
Cardiac Output Monitoring ◽  
Least Mean Square ◽  
Clinical Environment ◽  
The Real

Impedance Cardiography (ICG) evaluation facilitates the volume of heart stroke in the sudden cardiac arrest. It is a noninvasive method for measurement of stroke volume, cardiac output monitoring and observing the hemodynamic parameters by changes in the body blood volume. Bloodvolume changes caused due to various physiological processes is extracted in the form of the variations in the impedance of the body segment. In the real time clinical environment during the extraction the ICG signals are influenced with several artifacts.As these artifacts are not stationary in nature, we can’t predict their characteristics. Hence,we developed several hybrid adaptive filtering mechanisms to improve the ICG signals resolution. Least mean square (LMS) algorithm is the basic enhancement technique in the adaptive filtering. However, in the non-stationery situation the LMS algorithm suffers with low rate of convergence and weight drift problems. In this paper we developed some hybrid variantsof LMS algorithm those are Leaky LMS (LLMS) for ICG signal enhancement. More over to progress the convergence rate, filtering capability and to reduce the computational complexity we also developed various sign versions of LLMS algorithms. The sign variants of LLMS algorithms are sign regressor LLMS (SRLLMS), Sign LLMS (SLLMS), and Sign Sign LLMS (SSLLMS). Severaladaptive signal enhancement units (ASEUs) are developed based on adaptive algorithms and performance is evaluated on the real ICG signal taken from MIT-BIT database. To ensure the efficiency of these algorithms, four experiments were performed to eliminate the various artifacts such as sinusoidal artifacts (SA), respiration artifacts (RA), muscle artifacts (MA) and electrode artifacts (EA). Among these techniques, the ASEU associated with SRLLMS performs better in the artifacts filtering process. The signal to noise ratio improvement (SNRI) for this algorithm is calculated as 9.3388 dBs, 5.7514 dBs, 8.4449 dBs and 8.7358 dBs respectively for SA, RA, MA and EA. Hence, the SRLLMS based ASEUs are more suitable in ICG signal filtering in real time health care sensing systems.

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.

A complementary least-mean-square algorithm of adaptive filtering for SQUID based magnetocardiography

2005 ◽  
Vol 14 (6) ◽  
pp. 1095-1100 ◽  
Author(s):  
Li Zhuo ◽  
Chen Geng-Hua ◽  
Zhang Li-Hua ◽  
Yang Qian-Sheng ◽  
Feng Ji
Keyword(s):  
Adaptive Filtering ◽  
Mean Square ◽  
Least Mean Square ◽  
Least Mean Square Algorithm

An Improved ZA-LMS Algorithm for Sparse System Identification with Variable Sparsity

2014 ◽  
Vol 602-605 ◽  
pp. 2415-2419 ◽  
Author(s):  
Hui Luo ◽  
Yun Lin ◽  
Qing Xia
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Sparse System ◽  
Sparse System Identification ◽  
Least Mean Square Algorithm ◽  
Improved Algorithm

The standard least mean square algorithm does not consider the sparsity of the impulse response,and the performs of the ZA-LMS algorithm deteriorates ,as the degree of system sparsity reduces or non-sparse . Concerning this issue ,the ZA-LMS algorithm is studied and modified in this paper to improve the performance of sparse system identification .The improved algorithm by modify the zero attraction term, which attracts the coefficients only in a certain range (the “inactive” taps), thus have a good performance when the sparsity decreases. The simulations demonstrate that the proposed algorithm significantly outperforms then the ZA-LMS with variable sparisity.

scholarly journals SPEECH RECOGNITION USING SOM AND ACTUATION VIA NETWORK IN MATLAB

2014 ◽  
pp. 191-194
Author(s):  
A. SUBASH CHANDAR ◽  
S. SURIYANARAYANAN ◽  
M. MANIKANDAN
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Acoustic Noise ◽  
Lms Algorithm ◽  
Prototype System ◽  
Mean Square ◽  
Least Mean Square ◽  
Self Organizing Maps ◽  
Voice Signal ◽  
The Voice

This paper proposes a method of Speech recognition using Self Organizing Maps (SOM) and actuation through network in Matlab. The different words spoken by the user at client end are captured and filtered using Least Mean Square (LMS) algorithm to remove the acoustic noise. FFT is taken for the filtered voice signal. The voice spectrum is recognized using trained SOM and appropriate label is sent to server PC. The client and the server communication are established using User Datagram Protocol (UDP). Microcontroller (AT89S52) is used to control the speed of the actuator depending upon the input it receives from the client. Real-time working of the prototype system has been verified with successful speech recognition, transmission, reception and actuation via network.

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