A Noise Reduction Method Based on Modified Least Mean Square Algorithm of Real Time Speech Signals With The Help of Wiener Filter (original) (raw)
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A Noise Reduction Method Based on Modified LMS Algorithm of Real time Speech Signals
WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL
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.
geocities.ws
The performance of Wiener-Hopf and Least Mean Square (Online and Batch) methods are considered for recovering the desired signal buried under noise using adaptive noise cancellation techniques. A reference signal and a desired signal are available. To combat the noisy environment an adaptive noise cancellation filter is developed. This filter tries to converge in least mean square sense to the optimal Wiener-Hopf solution to filter the noise from the signal. In the analysis part the weight and learning tracks are shown. Effects of leakage and effects of interchanging the primary and reference inputs to the adaptive filter are also discussed. In the end a comparison is drawn on intelligibility of the recovered signal from both the methods.
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 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.
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.
Study of the widely linear Wiener filter for noise reduction
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
This paper develops a new widely linear noise-reduction Wiener filter based on the variance and pseudo-variance of the short-time Fourier transform coefficients of speech signals. We show that this new noise-reduction filter has many interesting properties, including but not limited to: 1) it causes less speech distortion as compared to the classical noise-reduction Wiener filter; 2) its minimum meansquared error (MSE) is smaller than that of the classical Wiener filter; 3) it can increase the subband signal-to-noise ratio (SNR), while the classical Wiener filter has no effect on the subband SNR for any given signal frame and subband.
Reduction of Noise in Speech Signal Processing and Reconstruction of Signal
IAEME PUBLICATION, 2020
Noise is a signal where information is converted into voltage variation. The cause of noise is another signal of voltage variation which has occurred from a source which is not authorised known as unwanted or undesired signal. It may enter our communication system in various stages through a microphone from external sources such as wind, human disturbances, etc. This paper dilineates the LMS adaptive method used for audio quality amplification by minimising the noise in speech signal processing. The least mean square and leaky least mean square were the two basic adaptive algorithms has been executed to turn down the noise within the range of speech signal. This combined system has more efficiency and can be used in various places such as auditorium, etc for various purposes where the noise is the major problem. The software can be used easily to detect and minimize the noise using applications. Widrow's adaptive noise cancellation technique with some improvements in the matlab code and the signal to noise ratio improvements were presented in this paper.
Simulation and Comparative Analysis of LMS and RLS Algorithms Using Real Time Speech Input Signal
Global Journal of Research In Engineering, 2010
In practical application, the statistical characteristics of signal and noise are usually unknown or can't have been learned so that we hardly design fix coefficient digital filter. In allusion to this problem, the theory of the adaptive filter and adaptive noise cancellation are researched deeply. According to the Least Mean Squares (LMS) and the Recursive Least Squares (RLS) algorithms realize the design and simulation of adaptive algorithms in noise canceling, and compare and analyze the result then prove the advantage and disadvantage of two algorithms .The adaptive filter with MATLAB are simulated and the results prove its performance is better than the use of a fixed filter designed by conventional methods.
Enhancing speech using an Adaptive Wiener Filter based algorithm
International Journal of Advances in Computing and Information Technology, 2012
Speech can be expressed as a mechanism of expressing thoughts and ideas using vocal sounds. In humans, speech signals are generated at vocal cords, travelled through the vocal tract, and finally produced & transmitted through speaker's mouth. These speech signals then usually travels through air or other mediums to the listener's ear, where it acts as pressure waves. The bandwidth of speech signal is around 4 KHz. The noise produced by various ambient sources such as vehicles normally lies in this frequency range. Therefore, speech signals get easily distorted by the ambient noise. These distorted or degraded speech signals are called noisy speech signals. This paper focuses on speech processing (in particularly speech enhancement) of the noisy speech signals. This is very important as speech is the most commonly used way of communication and interaction between humans; however, it is very complex to understand. Therefore, this paper proposes an adaptive algorithm based on Wiener filter for speech enhancement. The proposed adaptive Wiener filter depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics (mean and variance). The adaptive Wiener filter is implemented in time domain rather than in frequency domain. This is done to accommodate the random speech signal. The proposed method is compared to the traditional Wiener filter and the spectral subtraction methods.