Earticle

Elimination of noise from the signal is the major task in signal processing applications. Wiener filter removes noise efficiently but it requires large number of computations and it was updated with speed issue with adaptive filter. Adaptive filter has several algorithms to remove noise from the signal. This paper performs cancellation of noise from the signal using wiener filter and adaptive filter algorithms namely LMS, NLMS and RLS algorithms in real time environment. All these methods are compared using several parameters like step size, mean and variance of noise, mean square error, signal to noise ratio, speed, no. of. Iterations etc. In the existence work, the authors have compared the performance of the wiener filter & LMS algorithm in real time environment with sinusoidal input. This paper is extended by comparing different adaptive filter algorithms with the input taken in real time environment. It is observed that RLS algorithm performs noise cancellation better than all other algorithms. But it has high degree of complexity & cost while NLMS algorithm has moderate speed of performance and it is quietly chosen for several applications.

In this paper, we propose a new VSS-NLMS algorithm through a simple modification of the conventional NLMS algorithm, which leads to a low complexity algorithm with enhanced performance. The step size of the proposed algorithm becomes smaller as the error signal is getting orthogonal to the input vector. We also show that the proposed algorithm is an approximated normalized version of the KZ-algorithm and requires less computation than the KZ-algorithm. We carried out a performance comparison of the proposed algorithm with the conventional NLMS and other VSS algorithms using an adaptive channel equalization model. It is shown that the proposed algorithm presents good convergence characteristics under both stationary and non-stationary environments despites its low complexity.

This paper presents a performance analysis of an improved adaptive algorithm proposed by the authors recently. It is based on the normalized least mean square (NLMS) algorithm, which Is one of the major techniques to adapt the cofficients of a transversal filter. Generally, the performance of an adaptive algorithm is often discussed by investigating the mis-adjustment. In this paper, unlike these approaches, a novel analytical method is considered. letting the parameters so that the residual mean square error (MSE) after the convergence of the algorithm is equal to that of the NLMS algorithm, the MSE level is compared. It is shown that the theoretical analysis is agreed with the simulation results.

통신망에서 최적 적응 반향제거기(Echo Canceller; EC)는 반향성분이 길게 존재하는 환경에서도 실시간으로 동작할 수 있도록 알고리즘이 간결하여야 하며, 시간에 따라 빠르게 변하는 동특성의 반향경로에서도 동작을 보장할 수 있도록 빠른 수렴특성을 갖아야 한다. 또한, 전화망에서 수십 [ms] 이상의 지연이 발생 할 경우에도 반향제거 성능이 우수해야 한다. 본 논문에서는 이러한 조건을 만족시키기 위해 오차의 크기에 따라 수렴속도를 가변시키는 오차적응 NLMS(Error-Adaptive NLMS) 알고리즘을 제안하였으며, 시뮬레이션을 통해 일반적으로 사용되는 LMS(Least Mean Square) 알고리즘과 이를 개선한 NLMS(Normalized LMS) 알고리즘과 성능을 비교하였다.

본 논문에서는 계산량이 크게 증가하지 않으면서 음향 반향 제거기의 성능을 향상시키는 잡음에 강건한 알고리즘을 제안한다. 제안하는 알고리즘은 NLMS 알고리즘을 기반으로 적응 상수를 기준 입력신호와 마이크 입력신호의 전력합으로 정규화한다. 마이크 입력으로 근단화자의음성신호와 잡음이 입력되면 그 전력만큼 적응 상수가 작아지기 때문에 이들 주변 잡음신호에 의한 계수 오조정을 줄일 수있다. 따라서 제안하는 알고리즘은 동시통화와 주변 잡음이 있는 상황에서 반향 제거 기능을 잘 수행한다. 제안하는 알고리즘의 수렴 속도는 반향 신호가 10~20 dBSPL 정도 감쇠되기 때문에 NLMS 알고리즘에 비해서 크게 느려지지 않는다. 본 논문에서는 제안하는 알고리즘의 성능을 분석하고 기존의 알고리즘과 비교한다.

In this paper, a new robust adaptive algorithm is proposed to improve the performance of AEC without computational burden. The proposed adaptive algorithm is based on NLMS algorithm, and its step-size is varied with the reference input signal power and the desired signal power. Its step-size is normalized by the sum of the powers of the reference input signal and the desired signal. When the near-end speaker's speech and noise are applied into the microphone, the step-size becomes small and the misalignment of coefficients are reduced. The convergence speed is comparable to NLMS algorithm at AEC application because the echo signals are attenuated about 10∼20 dBSPL. The characteristics of this algorithm is also analyzed and compared with conventional ones in this paper.

본 논문에서는 전이중 디지탈 가입자 선로 모뎀에서 사용될 수 있는 새로운 echo canceller를 제안하였다. 제안된 echo canceller는 적응알고리즘으로 NLMS알고리즘에 기반을 둔 병렬적응(parallel adaptation) NLMS 알고리즘을 사용하였다. 2개의 NLMS과정으로부터 비정체성 대 부가잡음비의 추정치를 구하고 이로부터 최적수렴제어 파라미터를 선택하는 PA-NLMS알고리즘을 유도하였다. 컴퓨터 시뮬레이션 결과 제안된 PA-NLMS 알고리즘은 가장 빠른 NLMS의 수렴속도와 비슷하였으나 misadjustment at convergence(MAC) 성능에서는 상당히 우수함을 보였다.

This paper proposes a new echo canceller that can be used in a full-duplex digital subscriber loop modem. The proposed echo canceller uses a NLMS-based parallel adaptation NLMS (PA-NLMS) algorithm. The PA-NLMS algorithm that an estimate of the nonstationarity to additive noise ratio (NNR) gained from two distinct NLMS processes is used to select the value of NLMS convergence-controlling parameter has been developed. Numerical results based on computer simulation show that the proposed algorithm has a convergence rate approaching that of the fastest possible NLMS process while improving on its MAC performance considerably.

Adaptive nois canceller can extract the noiseremoved spech in noisy speech signal by adapting the filter-coefficients to the background noise environment. A kind of LMS algorithm is one of the most popular adaptive algorithm for noise cancellation due to low complexity, good numerical property and the merit of easy implementation. However there is the matter of increasing misadjustment at voiced speech signal. Therefore the demanded speech signal may be extracted. In this paper, we propose a fast and noise robust wavelet packet adaptive noise canceller with NLMS-SUM method LMS combined algorithm. That is, we decompose the frequency of noisy speech signal at the base of the proposed analysis tree structure. NLMS algorithm in low frequency band can efficiently dliminate the effect of the low frequency noise and SUM method LMS algorithm at each high frequency band can remove the high frequency nosie. The proposed wavelet packet adaptive noise canceller is enhanced the more in SNR and according to Itakura-Satio(IS) distance, it is closer to the clean speech signal than any other previous adaptive noise canceller.