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.

scholarly journals Adaptive Noise Cancellation Algorithms Implemented onto FPGA-Based Electrical Impedance Tomography System

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Marlin Ramadhan Baidillah ◽  
Zengfeng Gao ◽  
Al-Amin Saichul Iman ◽  
Masahiro Takei
Keyword(s):  
Electrical Impedance ◽  
Random Noise ◽  
Noise Cancellation ◽  
Noise Signal ◽  
Time Varying ◽  
Mean Square ◽  
Least Mean Square ◽  
Adaptive Noise Cancellation ◽  
Impedance Tomography ◽  
Adaptive Noise

Electrical Impedance Tomography (EIT) as a non-invasive of electrical conductivity imaging method commonly employs the stationary-coefficient based filters (such as FFT) in order to remove the noise signal. In the practical applications, the stationary-coefficient based filters fail to remove the time-varying random noise which leads to the lack of impedance measurement sensitivity. In this paper, the implementation of adaptive noise cancellation (ANC) algorithms which are Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) filters onto Field Programmable Gate Array (FPGA)-based EIT system is proposed in order to eliminate the time-varying random noise signal. The proposed method was evaluated through experimental studies with biomaterial phantom. The reconstructed EIT images with NLMS is better than the images with LMS by amplitude response AR = 12.5%, position error PE = 200%, resolution RES = 33%, and shape deformation SD = 66%. Moreover, the Analog-to-Digital Converter (ADC) performances of power spectral density (PSD) and the effective number of bit ENOB with NLMS is higher than the performances with LMS by SI = 5.7 % and ENOB = 15.4 %. The results showed that implementing ANC algorithms onto FPGA-based EIT system shows significantly more accurate image reconstruction as compared without ANC algorithms implementation.

scholarly journals Adaptive Noise Cancellation for speech Employing Fuzzy and Neural Network

2011 ◽  
Vol 7 (2) ◽  
pp. 94-101
Author(s):  
Mohammed Miry ◽  
Ali Miry ◽  
Hussain Khleaf
Keyword(s):  
Neural Network ◽  
Adaptive Filtering ◽  
Communication Systems ◽  
Digital Signal ◽  
Noise Cancellation ◽  
Lms Algorithm ◽  
Adaptive Noise Cancellation ◽  
Long Distance ◽  
Wide Range ◽  
Adaptive Noise

Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications such as noise cancellation. Noise cancellation is a common occurrence in today telecommunication systems. The LMS algorithm which is one of the most efficient criteria for determining the values of the adaptive noise cancellation coefficients are very important in communication systems, but the LMS adaptive noise cancellation suffers response degrades and slow convergence rate under low Signal-to-Noise ratio (SNR) condition. This paper presents an adaptive noise canceller algorithm based fuzzy and neural network. The major advantage of the proposed system is its ease of implementation and fast convergence. The proposed algorithm is applied to noise canceling problem of long distance communication channel. The simulation results showed that the proposed model is effectiveness.

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