An Efficient Adaptive Noise Cancellation Scheme Using ALE and NLMS Filters (original) (raw)

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,

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.

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

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.

Review Paper on Noise Cancellation using Adaptive Filters

Deepanjali Jain, 2022

This paper reviews the past and the recent research based on adaptive noise cancellation system using Adaptive filter algorithms. Adaptive noise cancellation is a wide area of research in the field of communication and is used for noise reduction in speech signals. In many applications, the change in the received signals could be very fast which requires the use of adaptive algorithms that converge rapidly. This paper deals with cancellation of noise in speech signal using Least Mean Square (LMS) adaptive algorithms that provides efficient performance with less computational complexity.

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

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.

A Family of Adaptive Filter Algorithms in Noise Cancellation for Speech Enhancement

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 enhancement using the two new adaptive filtering algorithms named fast affine projection algorithm and fast Euclidean direction search algorithms for attenuating noise in speech signals. The simulation results demonstrate the good performance of the two new algorithms in attenuating the noise. Sayed.A. Hadei was born in Ardebil, Iran in 1985. He has been working towards M.Sc degree in Electrical Engineering emphais on communication Systems from Tarbiat Modares University Tehran, Iran. His current research interest include digital filter theory, adaptive signal processing algorithms, bayesian signal processing, wireless communication, MIMO-OFDM systems and estimation theory.

Adaptive Noise Cancellation for Speech Processing in Real Time Environment

2013

The most common problem in speech processing is the interference noise in speech signals. Interference can come from acoustical sources such as ventilation equipment, traffic crowds and commonly reverberation and echoes. The basic adaptive algorithm is the LMS algorithm but its’ major drawback is the excess mean square error increase linearly with the desired signal power. We proposed an algorithm for adaptive noise cancellation using normalized differential least mean square NDLMS algorithm in real time environment. In this paper NDLMS algorithm is proposed to deal with situation when the desired signal is strong for example, speech signal. Simulations were carried out using real speech signal with different noise power levels. Results demonstrate the superiority of the proposed NDLMS algorithm over LMS algorithm in achieving much smaller steady state excess mean square error.