The Design of Adaptive Noise Cancellation Filter Based on DSP Chip

2014 ◽  
Vol 971-973 ◽  
pp. 1786-1790
Author(s):  
Xiu Min Wang ◽  
Ting Ting Li ◽  
Liang Shan
Keyword(s):  
Digital Filters ◽  
Noise Cancellation ◽  
Noise Signal ◽  
Lms Algorithm ◽  
Speech Communication ◽  
Adaptive Noise Cancellation ◽  
Change Characteristics ◽  
The Common ◽  
Adaptive Noise

The speech signal usually could not be extracted correctly from the digital speech communication system with strong interference. As for this kind of system, the common fixed coefficient digital filters (FIR, IIR) are unable to achieve the best effect of filtering. Whereas the adaptive filter could extract the available signals properly by adjusting the filter coefficient automatically without knowing the change characteristics of the noise signal. In this paper, we designed an adaptive noise cancellation filter based on LMS algorithm on the DSP chip and verification of the filter was done on the TMS320C5509 platform. The results show that the adaptive noise cancellation designed in this paper could extract the available signals properly and improve the quality of the speech communication.

Study on Anti-Noise Materials with Multi-Level Filter Based on the Adaptive Noise Cancellation System

2014 ◽  
Vol 886 ◽  
pp. 390-393
Author(s):  
Jing Mo ◽  
Wei He ◽  
Dan Su ◽  
Jing Wei Wu
Keyword(s):  
Noise Cancellation ◽  
Noise Signal ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Adaptive Noise Cancellation ◽  
Step Size ◽  
Filter Performance ◽  
Multi Level ◽  
Adaptive Noise ◽  
Filtering Effect

It presents the Multi-level filters idea of the adaptive noise cancellation system based on the fact that the adaptive noise cancellation system cant filter out noise signal completely. According to the linear combination and the variable step-size LMS algorithm, it analyzes the effects of the two level filters. Theory analyzing and simulation results prove that the multi-level filter can get a better the filtering effect than the one-filter, which improves the filter performance in terms of the fast convergence speed, tracking speed and the low maladjustment error. And the anti-noise materials with multi-level filter based on the adaptive noise cancellation system has the good de-noising ability of noisy signals.

scholarly journals A Research of Noise Estimation and Removal Techniques for Speech Signal

2019 ◽  
Vol 8 (2S3) ◽  
pp. 718-721
Keyword(s):  
Adaptive Filter ◽  
Speech Signal ◽  
Active Noise Control ◽  
Noise Cancellation ◽  
Noise Signal ◽  
Noise Estimation ◽  
Control Technique ◽  
Adaptive Noise Cancellation ◽  
Active Noise ◽  
Adaptive Noise

In this paper, authors made an attempt to implement the active noise control technique (ANC) to decrease the amplitude of noise communicating through the environment using an electro-acoustic (EA) system with the help of measurement sensors such as microphones and output actuators such as loudspeakers. In general, the noise signal is generated from ambient; therefore, it is easy to detect the noise in the vicinity of its source. The main objective of developing the ANC system is to generate an “anti-noise" that reduce the unwanted noise in a desired quiet region using an appropriate adaptive filter. The simulations were performed in the MATLAB 2015 environment and satisfactory results were obtained using the proposed technique. The problem under study is different from traditional adaptive noise cancellation techniques in two ways. Firstly, it is not possible to measure the desired response of a signal directly measured; only the signal with reduced magnitude is present. Secondly, the ANC system is required to take into consideration the secondary loudspeaker-to-microphone error (LME) path in its adaptation.

Research and Design of Adaptive Noise Cancellation Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 1942-1945
Author(s):  
Jie Zhang ◽  
Shi Qi Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Noise Cancellation ◽  
Biological Species ◽  
Lms Algorithm ◽  
Adaptive Noise Cancellation ◽  
Step Size ◽  
Swarm Optimization ◽  
Adaptive Noise

Particle swarm optimization (PSO) is a kind of evolutionary computation technology which simulates the behavior of biological species. The essence of adaptive noise cancellation (ANC) is adjust the weight value of filter based on the input signals, the LMS algorithm is commonly used in this system, However, the convergence behavior and maladjustment of the LMS algorithm is seriously affected by the step-size μ, and the optimum value of μ cannot be determined easily, In this paper, Particle Swarm Optimization with linear decreasing inertia weight is proposed to solve the filter problem instead of LMS, taking the FIR filter of ANC as example, the simulation shows that ANC based on the PSO algorithm is better than classic ANC based on the LMS algorithm, and it gives the satisfactory results.

Use of Adaptive Digital Signal Processing to Improve Speech Communication for Normally Hearing and Hearing-Impaired Subjects

1988 ◽  
Vol 31 (2) ◽  
pp. 265-271 ◽  
Author(s):  
Richard W. Harris ◽  
Robert H. Brey ◽  
Martin S. Robinette ◽  
Douglas M. Chabries ◽  
Richard W. Christiansen ◽  
...  
Keyword(s):  
Word Recognition ◽  
Digital Signal ◽  
Noise Cancellation ◽  
Noise Signal ◽  
Hearing Impaired ◽  
Speech Communication ◽  
Signal To Noise ◽  
Speech Spectrum ◽  
Babble Noise ◽  
Adaptive Noise

A two microphone adaptive digital noise cancellation technique was used to improve word-recognition ability of normally hearing and hearing-impaired subjects in the presence of varying amounts of multitalker speech babble noise and speech spectrum noise. Signal-to-noise ratios varied from -8 dB to + 12 dB in 4 dB increments. The adaptive noise cancellation technique resulted in reducing both the speech babble and speech spectrum noises 18 to 22 dB. This reduction in noise resulted in average improvements in word recognition, at the poorest signal-to-noise ratios, ranging from 37% to 50% for the normally hearing subjects and 27% to 40% for the hearing-impaired subjects. Improvements in word recognition in the presence of speech babble noise as a result of adaptive filtering were just as large or larger than improvements found in the presence of speech spectrum noise. The amount of improvement of word-recognition scores was most pronounced at the least favorable signal-to-noise ratios.

Speech Signal Denoise Based on LMS Algorithm in Simulink

2011 ◽  
Vol 225-226 ◽  
pp. 453-456 ◽  
Author(s):  
Yan Ping He
Keyword(s):  
Prior Knowledge ◽  
Adaptive Filter ◽  
Speech Signal ◽  
Noise Cancellation ◽  
Lms Algorithm ◽  
Adaptive Noise Cancellation ◽  
Signal Simulation ◽  
Simulation Results ◽  
Adaptive Noise

The adaptive noise cancellation system by LMS algorithm need not to know the prior knowledge of input speech signal and noise, and can carry out denoise. In this paper, we present a general approach to using Simulink to build adaptive filter which may denoise for noise added speech signal. Simulation results show that this method has the good suppression ability for the noise of collection speech signal.

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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