Comparative Study of Wiener-Hopf Solution and LMS Algorithm for Adaptive Noise Cancellation of Noisy Speech (original) (raw)

NOISE SUPPRESSION IN SPEECH SIGNALS USING ADAPTIVE ALGORITHMS

Adaptive Filtering is a widely researched topic in the present era of communications. When the received signal is continuously corrupted by noise where both the received signal and noise change continuously, then arises the need for adaptive filtering. The heart of the adaptive filter is the adaptive algorithm. This paper deals with cancellation of noise on speech signals using two algorithms-Least Mean Square (LMS) algorithm and Recursive Least Squares (RLS) algorithm with implementation in MATLAB. Comparisons of algorithms are based on SNR and tap weights of FIR filter. The algorithms chosen for implementation which provide efficient performances with less computational complexity.

Adaptive Noise Cancellation in Speech Signal

2018

Speech has always been one of the most important carriers of information for people. It becomes a challenge to maintain its quality. In many application of noise cancellation, the changes in signal characteristics could be quite fast.So, To eliminate background noise from the main signal, adaptive filtering techniques should be used.The adaptive noise cancelling is an alternative method of estimating signals corrupted by additive noise or interference.The principle advantages of the method are its adaptive capability, its low output noise, and its low signal distortion. This paper describes the use of adaptive algorithms to reduce unwanted noisy signal, thus increasing communication quality.

COMPARATIVE EVALUATION OF ANN AND LMS BASED ALGORITHMS FOR ADAPTIVE NOISE CANCELLATION

The major hindrance to effective speech communication is the presence of surrounding noise and interference that tend to mask and corrupt the intelligent part of the signal. To remove the noisy components of the speech signals, adaptive noise cancellation (ANC) technique has been found efficient. In literature, several algorithms have been developed for filter coefficients adjustment for ANC systems, one of which is the least mean square (LMS). In this study, artificial neural network (ANN) based ANC technique has been proposed and compared with the conventional LMS. The algorithms were implemented and tested with a real time noisy speech signal. Simulation results are also presented to support the experimental and mathematics analysis. The performance analysis has been evaluated in terms of the means square error (MSE) of the algorithms. The developed ANN based algorithm gives a better MSE value compared to LMS when applied on speech signal.

Audio Noise Cancellation using Wiener Filter based LMS Algorithm using LabVIEW

In this paper we designed a least mean square (LMS) adaptive filter to remove the unwanted noise which might occur during music recordings, echo in telephone networks, etc. Generally all LMS algorithm starts with an assumption of weight vector as zero initially and iteration continues till the error is minimized till its optimum level. This takes much more time to compute the optimized coefficients. In our work we designed the Wiener Filter using Wiener-Hopf equation. Then the LMS algorithm is used to optimize the coefficients. In this method it is observed that less number of iterations is sufficient. Since Wiener-Hopf equation is basically considered for the problem of designing the filter that would produce the minimum mean square error of the desired signal, this will produce the optimized estimate.

Adaptive noise canceling for speech signals

Acoustics, Speech and Signal Processing, IEEE …, 1978

Abgtruct-A least mean-square (LMS) adaptive filtering approach has been formulated for removing the deleterious effects of additive noise on the speech signal. Unlike the classical LMS adaptive filtering scheme, the proposed method is designed to cancel out the clean speech signal. This method takes advantage of the quasi-periodic nature of the speech signal to form an estimate of the clean speech signal at time t from the value of the signal at time t minus the estimated pitch period. For additive white noise distortion, preliminary tests indicate that the method improves the perceived speech quality and increases the signalto-noise ratio (SNR) by 7 dB in a 0 dB environment. The method has also been shown to partially remove the perceived granularity of CVSD coded speech signals and to lead to an improvement in the linear prediction analysis/synthesis of noisy speech,

Speech Signal Enhancement Using Adaptive Noise Cancellation Techniques

Speech signal enhancement is an important topic in speech processing where signal changes its characteristics with time depending on various conditions. An important problem that affects the signal enhancement is the background noise which is a major source of quality degradation in speech and audio signals. Adaptive noise cancellation algorithms are used to reduce this noise with relatively fast convergence as desired. Minimization techniques like LMS, NLMS and RLS are widely used due to its simplicity in computation and implementation. These algorithms are evaluated under several conditions like sensitivity for language, text gender and noise power. Certain parameters were designed to obtain the best performance under various conditions where the RLS algorithm has outperformed the other two algorithms when noise power is fixed and that noise power has more influence on the RLS algorithm.

Noise Cancellation In Speech Signal Processing Using Adaptive Algorithm

Speech has always been one of the most important carriers of information for people it becomes a challenge to maintain its high quality. In many application of noise cancellation, the changes in signal characteristics could be quite fast. This requires the utilization of adaptive algorithms, which converge rapidly. Least Mean Squares (LMS) and Normalized Least Mean Squares (NLMS) adaptive filters have been used in a wide range of signal processing application because of its simplicity in computation and implementation. The Recursive Least Squares (RLS) algorithm has established itself as the "ultimate" adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations of the algorithm are often associated with high computational complexity and/or poor numerical properties. Recently adaptive filtering was presented, have a nice tradeoff between complexity and the convergence speed. This paper describes a new approach for noise cancellation in speech signal using the new adaptive filtering algorithm named affine projection algorithm for attenuating noise in speech signals. The simulation results demonstrate the good performance of the new algorithm in attenuating the noise

Comparative Study of Different Algorithms for the Design of Adaptive Filter for Noise Cancellation

2014

The main goal of this paper is to study and to compare the performance of different adaptive filter algorithms for noise cancellation. Adaptive noise cancellation method is used for estimating a speech signal which is corrupted by an additive noise. The reference input containing noise is adaptively filtered and subtracted from the primary input signal to obtain the de-noised signal. The desired signal which is corrupted by an additive noise can be recovered by an adaptive noise canceller using Least Mean Square (LMS) algorithm, Data Sign algorithm, Leaky LMS algorithm and constrained LMS algorithm. A performance comparison of these algorithms based on Signal to Noise Ratio(SNR) is carried out using MATLAB. Keywords-Adaptive Filter, Adaptive algorithms, MATLAB, Noise cancellation System, SNR

A Novel LMS Algorithm Applied to Adaptive Noise Cancellation

IEEE Signal Processing Letters, 2009

In this letter, we propose a novel least-mean-square (LMS) algorithm for filtering speech sounds in the adaptive noise cancellation (ANC) problem. It is based on the minimization of the squared Euclidean norm of the difference weight vector under a stability constraint defined over the a posteriori estimation error. To this purpose, the Lagrangian methodology has been used in order to propose a nonlinear adaptation rule defined in terms of the product of differential inputs and errors which means a generalization of the normalized (N)LMS algorithm. The proposed method yields better tracking ability in this context as shown in the experiments which are carried out on the AURORA 2 and 3 speech databases. They provide an extensive performance evaluation along with an exhaustive comparison to standard LMS algorithms with almost the same computational load, including the NLMS and other recently reported LMS algorithms such as the modified (M)-NLMS, the error nonlinearity (EN)-LMS, or the normalized data nonlinearity (NDN)-LMS adaptation.