Creating 'zone of silence' condition: least mean square adaptive filter application for acoustic noise removal

2012 ◽  
Vol 4 (2/3/4) ◽  
pp. 179
Author(s):  
Udayanto Dwi Atmojo
Keyword(s):  
Adaptive Filter ◽  
Acoustic Noise ◽  
Noise Removal ◽  
Mean Square ◽  
Least Mean Square ◽  
Silence Condition

Improved least mean square adaptive filter algorithm

2013 ◽  
Vol 32 (7) ◽  
pp. 2078-2081
Author(s):  
Cheng-xi WANG ◽  
Yi-an LIU ◽  
Qiang ZHANG
Keyword(s):  
Adaptive Filter ◽  
Mean Square ◽  
Least Mean Square

scholarly journals FPGA based least mean square algorithm for noise cancellation in communication system

2018 ◽  
Vol 7 (3.3) ◽  
pp. 165
Author(s):  
Praveen Reddy ◽  
Dr Baswaraj Gadgay
Keyword(s):  
Adaptive Filter ◽  
Communication System ◽  
Noise Cancellation ◽  
Mean Square ◽  
Least Mean Square ◽  
Distributed Arithmetic ◽  
Negative Effect ◽  
Least Mean Square Algorithm ◽  
Design Comparison

We present modified Distributed Arithmetic (DA) based architecture for LMS Adaptive filter which has improved the throughput of the filter also area and power has been comparatively been reduced. As we know, the adaptive filter uses continuous recalculation and generation of new coefficients will generate the negative effect on the use of algorithm. We have used a special temporary LUT addressing technique has overcome the issues resulting in better performance and good results. In this paper, we have discussed about the adaptive filter and implementation of DA adaptive filter and also discussed the results obtained from the design. Comparison with traditional de-sign has also been done to show the effectiveness of the algorithm.   

scholarly journals Identical Instruments Detection using Least Mean Square Based Adaptive Filter

2019 ◽  
Vol 1201 ◽  
pp. 012013
Author(s):  
M Sa’adah ◽  
D P Wulandari ◽  
Y K Suprapto
Keyword(s):  
Adaptive Filter ◽  
Mean Square ◽  
Least Mean Square

scholarly journals Multiple Iteration of Weight Updates for Least Mean Square Adaptive Filter in Active Noise Control Application

2017 ◽  
Vol 95 ◽  
pp. 14006
Author(s):  
Rahimie Mustafa ◽  
Anuar Mikdad Muad ◽  
Shahrizal Jelani ◽  
Ahmad Nur Alifa Abdul Razap
Keyword(s):  
Adaptive Filter ◽  
Noise Control ◽  
Active Noise Control ◽  
Mean Square ◽  
Least Mean Square ◽  
Active Noise ◽  
Control Application

scholarly journals SPEECH RECOGNITION USING SOM AND ACTUATION VIA NETWORK IN MATLAB

2014 ◽  
pp. 191-194
Author(s):  
A. SUBASH CHANDAR ◽  
S. SURIYANARAYANAN ◽  
M. MANIKANDAN
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Acoustic Noise ◽  
Lms Algorithm ◽  
Prototype System ◽  
Mean Square ◽  
Least Mean Square ◽  
Self Organizing Maps ◽  
Voice Signal ◽  
The Voice

This paper proposes a method of Speech recognition using Self Organizing Maps (SOM) and actuation through network in Matlab. The different words spoken by the user at client end are captured and filtered using Least Mean Square (LMS) algorithm to remove the acoustic noise. FFT is taken for the filtered voice signal. The voice spectrum is recognized using trained SOM and appropriate label is sent to server PC. The client and the server communication are established using User Datagram Protocol (UDP). Microcontroller (AT89S52) is used to control the speed of the actuator depending upon the input it receives from the client. Real-time working of the prototype system has been verified with successful speech recognition, transmission, reception and actuation via network.

Noise Removal from EMG Signal Using Adaptive Enhanced Squirrel Search Algorithm

2020 ◽  
Vol 19 (04) ◽  
pp. 2050039
Author(s):  
B. Nagasirisha ◽  
V. V. K. D. V. Prasad
Keyword(s):  
Adaptive Filters ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Maximum Error ◽  
Noise Removal ◽  
Least Square ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Emg Signal

Electromyogram (EMG) signals are mostly affected by a large number of artifacts. Most commonly affecting artifacts are power line interference (PLW), baseline noise and ECG noise. This work focuses on a novel attenuation noise removal strategy which is concentrated on adaptive filtering concepts. In this paper, an enhanced squirrel search (ESS) algorithm is applied to remove noise using adaptive filters. The noise eliminating filters namely adaptive least mean square (LMS) filter and adaptive recursive least square (RLS) filters are designed, which is correlated with an ESS. This novel algorithm yields better performance than other existing algorithms. Here the performances are measured in terms of signal-to-noise ratio (SNR) in decibel, maximum error (ME), mean square error (MSE), standard deviation, simulation time and mean value difference. The proposed work has been implemented at the MATLAB simulation platform. Testing of their noise attenuation capability is also validated with different evolutionary algorithms namely squirrel search, particle swarm optimization (PSO), artificial bee colony (ABC), firefly, ant colony optimization (ACO) and cuckoo search (CS). The proposed work eliminates the noises and provides noise-free EMG signal at the output which is highly efficient when compared with existing methodologies. Our proposed work achieves 4%, 40%, 4%, 7%, 9% and 70% better performance than the literature mentioned in the results.

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