scholarly journals Adaptive Noise Cancellation from Speech Signals using Variablestep Sizealgorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 12041-12046
Keyword(s):  
Computational Complexity ◽  
Convergence Rate ◽  
Hearing Aids ◽  
Speech Signal ◽  
Speech Therapy ◽  
Noise Cancellation ◽  
Speech Signals ◽  
Good Resolution ◽  
Least Mean Square ◽  
Adaptive Noise

Noise cancellation from the speech signal is the most importanttask in applications like communications, hearing aids, speech therapy and many others. This allows providing good resolution speech signal to the user. The speech signals are mostlycontaminated due to the several natural as well as manmade noises. As the characteristics of these noises random in its nature filtering techniques with fixed coefficients are not suitable for noise cancellation task. Hence, in this work an adaptive noise canceller algorithmhas driven for enhancement of speech signal applications which has the capability to update its weight coefficients based on the statistical nature of the undesired component in the actual speech signal. In our experiments in order to achieve better convergence rate as well as filtering capability we propose Step Variable Least Mean Square (SVLMS) algorithm instead of constant step parameter. The computational complexity of the speech enhancement process is also a key aspect due to the excessive length of the speech signals in realistic scenario. Hence, to reduce the computational complexity of the proposed mechanism we used Sign Regressor SVLSM (SRSVLMS), which is a hybrid realization of familiar sign regressor algorithm and the proposed SVLMS. Using these two techniques noise cancellation models are developed and tested on real speech signals with unwanted noise contaminations. The experimental outputsconfirm that the SRSVLMS based speech signal enhancement unit performs better than its counterpart with respect to convergence rate, computational complexity and signal to noise ratio increment

scholarly journals LMS Adaptive Filters for Noise Cancellation: A Review

2017 ◽  
Vol 7 (5) ◽  
pp. 2520 ◽  
Author(s):  
Shubhra Dixit ◽  
Deepak Nagaria
Keyword(s):  
Computational Complexity ◽  
Convergence Rate ◽  
Adaptive Filters ◽  
Adaptive Algorithms ◽  
Noise Cancellation ◽  
Adaptive Noise Cancellation ◽  
The Past ◽  
Signal Characteristics ◽  
Adaptive Noise ◽  
Filter Algorithms

This paper reviews the past and the recent research on Adaptive Filter algorithms based on adaptive noise cancellation systems. In many applications of noise cancellation, the change in signal characteristics could be quite fast which requires the utilization of adaptive algorithms that converge rapidly. Algorithms such as LMS and RLS proves to be vital in the noise cancellation are reviewed including principle and recent modifications to increase the convergence rate and reduce the computational complexity for future implementation. The purpose of this paper is not only to discuss various noise cancellation LMS algorithms but also to provide the reader with an overview of the research conducted.

Implementation of Adaptive Noise Canceller System for Audio-Related Applications

2013 ◽  
Vol 3 (4) ◽  
pp. 51-67
Author(s):  
Swati S. Godbole ◽  
Sanjay B. Pokle
Keyword(s):  
Adaptive Filtering ◽  
Noise Cancellation ◽  
Mean Square ◽  
Least Mean Square ◽  
Adaptive Noise Cancellation ◽  
Noise Component ◽  
Wide Range ◽  
Adaptive Noise ◽  
Adaptive Noise Canceller ◽  
Filtering Techniques

This paper describes the performance of Adaptive Noise Cancellation system. Basic concept of adaptive noise canceller is to process signals from two input sources and to reduce the level of undesired noise with adaptive filtering techniques. Adaptive filtering techniques play vital role in wide range of applications. An implementation of adaptive noise cancellation system is used to remove undesired noise from a received signal for various audio related applications that has been developed and implemented by MATLAB. The dual channel adaptive noise cancellation system uses an adaptive filter with least mean square algorithm to cancel noise component from primary signal picked up by primary sensor. Various parameters such as convergence behavior, tracking ability of the algorithm, signal to noise ratio, mean square error etc. of ANC system are studied, analyzed for various applications of adaptive noise cancellation and the same are discussed in this paper.

RPSOVF Prediction Model for Speech Signal Series Based on UPSO

2019 ◽  
Vol 29 (06) ◽  
pp. 1950075
Author(s):  
Yumei Zhang ◽  
Xiangying Guo ◽  
Xia Wu ◽  
Suzhen Shi ◽  
Xiaojun Wu
Keyword(s):  
Prediction Model ◽  
Speech Signal ◽  
Signal Reconstruction ◽  
Volterra Model ◽  
Speech Signals ◽  
Mean Square ◽  
Least Mean Square ◽  
Nonlinear Prediction ◽  
Absolute Deviation ◽  
Low Performance

In this paper, we propose a nonlinear prediction model of speech signal series with an explicit structure. In order to overcome some intrinsic shortcomings, such as traps at the local minimum, improper selection of parameters, and slow convergence rate, which are always caused by improper parameters generated by, typically, the low performance of least mean square (LMS) in updating kernel coefficients of the Volterra model, a uniform searching particle swarm optimization (UPSO) algorithm to optimize the kernel coefficients of the Volterra model is proposed. The second-order Volterra filter (SOVF) speech prediction model based on UPSO is established by using English phonemes, words, and phrases. In order to reduce the complexity of the model, given a user-designed tolerance of errors, we extract the reduced parameter of SOVF (RPSOVF) for acceleration. The experimental results show that in the tasks of single-frame and multiframe speech signals, both UPSO-SOVF and UPSO-RPSOVF are better than LMS-SOVF and PSO-SOVF in terms of root mean square error (RMSE) and mean absolute deviation (MAD). UPSO-SOVF and UPSO-RPSOVF can better reflect trends and regularity of speech signals, which can fully meet the requirements of speech signal prediction. The proposed model presents a nonlinear analysis and valuable model structure for speech signal series, and can be further employed in speech signal reconstruction or compression coding.

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). 

scholarly journals A Research of Noise Estimation and Removal Techniques for Speech Signal

2019 ◽  
Vol 8 (2S3) ◽  
pp. 718-721
Keyword(s):  
Adaptive Filter ◽  
Speech Signal ◽  
Active Noise Control ◽  
Noise Cancellation ◽  
Noise Signal ◽  
Noise Estimation ◽  
Control Technique ◽  
Adaptive Noise Cancellation ◽  
Active Noise ◽  
Adaptive Noise

In this paper, authors made an attempt to implement the active noise control technique (ANC) to decrease the amplitude of noise communicating through the environment using an electro-acoustic (EA) system with the help of measurement sensors such as microphones and output actuators such as loudspeakers. In general, the noise signal is generated from ambient; therefore, it is easy to detect the noise in the vicinity of its source. The main objective of developing the ANC system is to generate an “anti-noise" that reduce the unwanted noise in a desired quiet region using an appropriate adaptive filter. The simulations were performed in the MATLAB 2015 environment and satisfactory results were obtained using the proposed technique. The problem under study is different from traditional adaptive noise cancellation techniques in two ways. Firstly, it is not possible to measure the desired response of a signal directly measured; only the signal with reduced magnitude is present. Secondly, the ANC system is required to take into consideration the secondary loudspeaker-to-microphone error (LME) path in its adaptation.

The Adaptive Filtering Algorithms for Engine’s Noise Cancellation in Speech Signals

2011 ◽  
Vol 130-134 ◽  
pp. 1323-1326
Author(s):  
Xiu Ying Zhao ◽  
Hong Yu Wang ◽  
De You Fu ◽  
Hai Shen Zhou
Keyword(s):  
Adaptive Filtering ◽  
Signal To Noise Ratio ◽  
Noise Cancellation ◽  
Speech Signals ◽  
Adaptive Noise Cancellation ◽  
Signal To Noise ◽  
Least Mean Squares ◽  
Signal Noise ◽  
Filtering Algorithm ◽  
Adaptive Noise

The presence of noise superimposed on a signal limits the receiver’s ability to correctly identify the intended signal. The principal of adaptive noise cancellation is to acquire an estimation of the unwanted interfering signal and subtract it from the corrupted signal. Noise cancellation operation is controlled adaptively with the target of achieving improved signal to noise ratio. This paper describes the Least Mean Squares (LMS) adaptive filtering algorithm. The algorithm was implemented in Matlab and was tested for noise cancellation in speech signals.

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