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Journal articles on the topic 'Signal processing Adaptive signal processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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1

Brewster, R. L. "Adaptive Signal Processing." Electronics and Power 32, no. 7 (1986): 545. http://dx.doi.org/10.1049/ep.1986.0314.

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2

Morgan, D. "Adaptive signal processing." IEEE Transactions on Acoustics, Speech, and Signal Processing 34, no. 4 (August 1986): 1017–18. http://dx.doi.org/10.1109/tassp.1986.1164869.

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3

Sibul, Leon H., and Teresa L. Dixon. "Environmentallly adaptive signal processing." Journal of the Acoustical Society of America 101, no. 5 (May 1997): 3157. http://dx.doi.org/10.1121/1.419091.

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4

Resnikoff, Howard L. "Wavelets and adaptive signal processing." Optical Engineering 31, no. 6 (1992): 1229. http://dx.doi.org/10.1117/12.57515.

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5

Haykin, Simon. "Guest Editorial: Adaptive Signal Processing." Optical Engineering 31, no. 6 (1992): 1143. http://dx.doi.org/10.1117/12.60706.

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6

Lindquist, C. "Book reviews - Adaptive signal processing." IEEE Control Systems Magazine 7, no. 4 (August 1987): 51. http://dx.doi.org/10.1109/mcs.1987.1105343.

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7

Harteneck, M., and R. W. Stewart. "Adaptive signal processing JAVA applet." IEEE Transactions on Education 44, no. 2 (May 2001): 6 pp. http://dx.doi.org/10.1109/13.925850.

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8

Wellstead, P. E. "Book Review: Adaptive Signal Processing." International Journal of Electrical Engineering & Education 23, no. 4 (October 1986): 375–76. http://dx.doi.org/10.1177/002072098602300429.

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9

Chakrabarti, N. B. "Transform Domain Adaptive Signal Processing." IETE Journal of Research 35, no. 2 (March 1989): 52–60. http://dx.doi.org/10.1080/03772063.1989.11436792.

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10

Chen, Walter Y., and Richard A. Haddad. "Dual mode adaptive signal processing." Computers & Electrical Engineering 18, no. 3-4 (May 1992): 261–75. http://dx.doi.org/10.1016/0045-7906(92)90019-a.

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11

Niang, Oumar, Abdoulaye Thioune, Éric Deléchelle, and Jacques Lemoine. "Spectral Intrinsic Decomposition Method for Adaptive Signal Representation." ISRN Signal Processing 2012 (December 13, 2012): 1–10. http://dx.doi.org/10.5402/2012/457152.

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We propose a new method called spectral intrinsic decomposition (SID) for the representation of nonlinear signals. This approach is based on the spectral decomposition of partial differential equation- (PDE-) based operators which interpolate the characteristic points of a signal. The SID’s components which are the eigenvectors of these PDE interpolation operators underlie the new signal decomposition-reconstruction method. The usefulness and the efficiency of this method is illustrated, in signal reconstruction or denoising aim, in some examples using artificial and pathological signals.
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12

Murano, K. "Adaptive Signal Processing Applied in Telecommunications." IFAC Proceedings Volumes 25, no. 14 (July 1992): 431–41. http://dx.doi.org/10.1016/s1474-6670(17)50772-7.

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13

Sztipanovits, Janos, Gabor Karsai, and Ted Bapty. "Self-adaptive software for signal processing." Communications of the ACM 41, no. 5 (May 1998): 66–73. http://dx.doi.org/10.1145/274946.274958.

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14

Stewart, R. W., M. Harteneck, and S. Weiss. "Interactive teaching of adaptive signal processing." Engineering Science & Education Journal 9, no. 4 (August 1, 2000): 161–68. http://dx.doi.org/10.1049/esej:20000404.

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15

Nejevenko, E. S., and A. A. Sotnikov. "Adaptive modeling for hydroacoustic signal processing." Pattern Recognition and Image Analysis 16, no. 1 (January 2006): 5–8. http://dx.doi.org/10.1134/s1054661806010020.

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16

Moustakides, G. V. "Locally optimum adaptive signal processing algorithms." IEEE Transactions on Signal Processing 46, no. 12 (1998): 3315–25. http://dx.doi.org/10.1109/78.735306.

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17

Garmatyuk, Dmitriy, and Matthew Brenneman. "Adaptive Multicarrier OFDM SAR Signal Processing." IEEE Transactions on Geoscience and Remote Sensing 49, no. 10 (October 2011): 3780–90. http://dx.doi.org/10.1109/tgrs.2011.2165546.

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18

McWhirter, J. G. "Algorithmic engineering in adaptive signal processing." IEE Proceedings F Radar and Signal Processing 139, no. 3 (1992): 226. http://dx.doi.org/10.1049/ip-f-2.1992.0028.

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19

Etter, Delores M. "An introduction to adaptive signal processing." Computers & Electrical Engineering 18, no. 3-4 (May 1992): 189–93. http://dx.doi.org/10.1016/0045-7906(92)90013-4.

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20

Bugallo, Mónica F., Luca Martino, and Jukka Corander. "Adaptive importance sampling in signal processing." Digital Signal Processing 47 (December 2015): 36–49. http://dx.doi.org/10.1016/j.dsp.2015.05.014.

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21

Bitmead, Robert R., C. Richard Johnson, and Clifford R. Pollock. "Optical adaptive signal processing: An appraisal." International Journal of Adaptive Control and Signal Processing 5, no. 2 (March 1991): 87–92. http://dx.doi.org/10.1002/acs.4480050202.

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22

Minasian, Robert A. "Ultra-Wideband and Adaptive Photonic Signal Processing of Microwave Signals." IEEE Journal of Quantum Electronics 52, no. 1 (January 2016): 1–13. http://dx.doi.org/10.1109/jqe.2015.2499729.

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23

Zou, Dong Lan. "Research on the Sensor Coarse Signal Processing Model Based on Adaptive Genetic Algorithm." Applied Mechanics and Materials 443 (October 2013): 342–45. http://dx.doi.org/10.4028/www.scientific.net/amm.443.342.

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With the rapid development of electronic information science and network transmission technology, the signal processing technology has been widely applied to various fields, which is the most important component of signal detection and transmission, and the key signal processing technology for processing sensor crude signals. Based on this, the experimental system of sensor coarse signal processing model is established, and in the experimental system, the transformer can carry out signal recognition for voltage and current, the use of PC microcontroller and embedded AD converter carries out analog / digital conversion for sensor crude signal. For the amplification process of sensor coarse signal, the use of adaptive genetic algorithm carries out mathematical modeling, the realization of the signal identification, acquisition and processing functions through software programming control. Finally, the intelligent processing of sensor coarse signal is successfully completed by the experiment system, and the signal processing effect is given as well.
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24

Cowan, C. F. N., R. F. Woods, J. P. Heron, P. Power, and F. J. Sweeney. "Advances in adaptive signal processing: totally adaptive systems." Annual Reviews in Control 25 (January 2001): 55–64. http://dx.doi.org/10.1016/s1367-5788(01)00006-2.

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25

Cowan, C. F. N., R. F. Woods, J. P. Heron, P. Power, and F. J. Sweeney. "Advances in Adaptive Signal Processing: Totally Adaptive Systems." IFAC Proceedings Volumes 31, no. 22 (August 1998): 185–94. http://dx.doi.org/10.1016/s1474-6670(17)35941-4.

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26

Raheem, Syed, and Dr Subhashish Bose. "Subband Adaptive Filter in Signal Processing Application." Revista Gestão Inovação e Tecnologias 11, no. 4 (August 4, 2021): 4096–109. http://dx.doi.org/10.47059/revistageintec.v11i4.2434.

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Owing to the powerful digital signal processors and the improvement of advanced edge adaptive algorithms there are an extraordinary number of various applications in which adaptive filters are utilized. Subband adaptive filtering algorithms can build the assembly pace of framework ID undertakings when the info signal is hued. The adaptive filter can filter the dubious noise signal, track the difference in the signal, and consistently change the boundaries to accomplish the ideal filtering impact. Another standardized subband adaptive filtering algorithm has been proposed, whose primary benefit is the lower computational intricacy when contrasted with best in class subband approaches, while keeping up comparable union execution. A connection between the adaptive subband coefficients and the ideal full band move work is determined, and the algorithm is demonstrated to create an asymptotically unprejudiced arrangement. The proficiency of the adaptive filters basically relies upon the plan procedure utilized and the algorithm of variation. The adaptive filters can be analogical plans, digital or blended which show their benefits and inconveniences, for instance, the analogical filters are low power consuming and fast response, however they address balance issues, which influence the activity of the variation algorithm.
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27

Ma, Bo Le, Jing Fang Cheng, and Wei Zhang. "Research of Vector Hydrophone Adaptive Signal Processing." Applied Mechanics and Materials 423-426 (September 2013): 2496–506. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2496.

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As a useful tool for line spectrum detection in underwater signal ,ALE has been used wildly. But there are still some problems to influence the effect of ALE. This paper gives three problems on ALE and analyses these.Then by the characteristics of vector hydrophone and a improved variable step size LMS,this paper constructs a cascade double input with variable step size based on vector hydrophone line spectrum enhancer . This algorithm restrains the noise of main channel twice ,meanwhile controls the noise in reference channel , so as to improve signal to noise ratio better. At the same time ,because of adopting the improved variable step size LMS, the steady-state error is reduced. From the results of simulation and experiment, the method presented in this paper can have a better effect of line spectrum enhancement.
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28

Perić, Zoran, Vlado Delić, Zoran Stamenković, and David Pokrajac. "Advanced Signal Processing and Adaptive Learning Methods." Computational Intelligence and Neuroscience 2019 (November 3, 2019): 1–2. http://dx.doi.org/10.1155/2019/5428615.

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29

Amari, S., and A. Cichocki. "Adaptive blind signal processing-neural network approaches." Proceedings of the IEEE 86, no. 10 (1998): 2026–48. http://dx.doi.org/10.1109/5.720251.

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30

Plataniotis, K. N., D. Androutsos, and A. N. Venetsanopoulos. "Adaptive fuzzy systems for multichannel signal processing." Proceedings of the IEEE 87, no. 9 (1999): 1601–22. http://dx.doi.org/10.1109/5.784243.

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31

Ise, Shiro, and Hideki Tachibana. "Active sound absorber using adaptive signal processing." Journal of the Acoustical Society of Japan (E) 17, no. 6 (1996): 305–10. http://dx.doi.org/10.1250/ast.17.305.

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32

Claasen, T., and W. Mecklenbrauker. "Adaptive techniques for signal processing in communications." IEEE Communications Magazine 23, no. 11 (November 1985): 8–19. http://dx.doi.org/10.1109/mcom.1985.1092451.

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33

Bisgaard, Nikolai, and Rob Anton Jurjen De Vries. "HEARING INSTRUMENT WITH ADAPTIVE DIRECTIONAL SIGNAL PROCESSING." Journal of the Acoustical Society of America 133, no. 2 (2013): 1198. http://dx.doi.org/10.1121/1.4790241.

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34

Rzeszewski, T. S., and P. H. Wyant. "Picture crispening by adaptive digital signal processing." IEEE Transactions on Consumer Electronics CE-33, no. 2 (1987): 71–76. http://dx.doi.org/10.1109/tce.1987.6446492.

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35

Besson, Olivier, and Stephanie Bidon. "Adaptive Processing With Signal Contaminated Training Samples." IEEE Transactions on Signal Processing 61, no. 17 (September 2013): 4318–29. http://dx.doi.org/10.1109/tsp.2013.2269048.

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36

Stewart, R. W., J. J. Soraghan, and T. S. Durrani. "Noncanonical FIR filters and adaptive signal processing." Electronics Letters 25, no. 6 (1989): 414. http://dx.doi.org/10.1049/el:19890285.

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37

Johnson, Paul A. "Overview of signal processing for adaptive optics." Digital Signal Processing 1, no. 1 (January 1991): 24–26. http://dx.doi.org/10.1016/1051-2004(91)90090-8.

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38

Kadlec, Jir̆í. "Adaptive system identification and signal processing algorithms." Automatica 31, no. 10 (October 1995): 1519–21. http://dx.doi.org/10.1016/0005-1098(95)90000-4.

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39

Chen, Junlin, and Lei Wang. "Energy-Adaptive Signal Processing Under Renewable Energy." Journal of Signal Processing Systems 84, no. 3 (November 2, 2015): 399–412. http://dx.doi.org/10.1007/s11265-015-1071-8.

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40

Naik, Sanjeev. "Advanced misfire detection using adaptive signal processing." International Journal of Adaptive Control and Signal Processing 18, no. 2 (March 2004): 181–98. http://dx.doi.org/10.1002/acs.789.

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41

Carpenter, Daniel P. "Adaptive Signal Processing, Hierarchy, and Budgetary Control in Federal Regulation." American Political Science Review 90, no. 2 (June 1996): 283–302. http://dx.doi.org/10.2307/2082885.

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Control over agency budgets is a critical tool of political influence in regulatory decision making, yet the causal mechanism of budgetary control is unclear. Do budgetary manipulations influence agencies by imposing resource constraints or by transmitting powerful signals to the agency? I advance and test a stochastic process model of adaptive signal processing by a hierarchical agency to address this question. The principal findings of the paper are two. First, presidents and congressional committees achieve budgetary control over agencies not by manipulating aggregate resource constraints but by transmitting powerful signals through budget shifts. Second, bureaucratic hierarchy increases the agency's response time in processing budgetary signals, limiting the efficacy of the budget as a device of political control. I also show that the magnitude of agency response to budgetary signals increased for executive-branch agencies after 1970 due to executive oversight reforms. I conclude by discussing the limits of budgetary manipulations as a device of political control and the response of elected authorities to adaptive signal processing by agencies.
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42

Ou, Jianping, Jun Zhang, and Ronghui Zhan. "Processing Technology Based on Radar Signal Design and Classification." International Journal of Aerospace Engineering 2020 (January 17, 2020): 1–19. http://dx.doi.org/10.1155/2020/4673763.

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It is well known that the application of radar is becoming more and more popular with the development of the signal technology progress. This paper lists the current radar signal research, the technical progress achieved, and the existing limitations. According to radar signal respective characteristics, the design and classification of the radar signal are introduced to reflect signal’s differences and advantages. The multidisciplinary processing technology of the radar signal is classified and compared in details referring to adaptive radar signal process, pulse signal management, digital filtering signal mode, and Doppler method. The transmission process of radar signal is summarized, including the transmission steps of radar signal, the factors affecting radar signal transmission, and radar information screening. The design method of radar signal and the corresponding signal characteristics are compared in terms of performance improvement. Radar signal classification method and related influencing factors are also contrasted and narrated. Radar signal processing technology is described in detail including multidisciplinary technology synthesis. Adaptive radar signal process, pulse compression management, and digital filtering Doppler method are very effective technical means, which has its own unique advantages. At last, the future research trends and challenges of technologies of the radar signals are proposed. The conclusions obtained are beneficial to promote the further promotion applications both in theory and practice. The study work of this paper will be useful for choosing more reasonable radar signal processing technology methods.
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43

Priya, L., and A. Kandaswamy. "Application of Adaptive Signal Processing for Electrocardiogram Signal Analysis: Interference Cancellation." Journal of Medical Imaging and Health Informatics 6, no. 2 (April 1, 2016): 499–505. http://dx.doi.org/10.1166/jmihi.2016.1719.

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44

Hasan, Shazia, P. K. Dash, and S. Nanda. "A signal processing adaptive algorithm for nonstationary power signal parameter estimation." International Journal of Adaptive Control and Signal Processing 27, no. 3 (March 28, 2012): 166–81. http://dx.doi.org/10.1002/acs.2287.

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45

Vollmer, M., and J. Götze. "An Adiabatic Architecture for Linear Signal Processing." Advances in Radio Science 3 (May 13, 2005): 325–29. http://dx.doi.org/10.5194/ars-3-325-2005.

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Abstract. Using adiabatic CMOS logic instead of the more traditional static CMOS logic can lower the power consumption of a hardware design. However, the characteristic differences between adiabatic and static logic, such as a four-phase clock, have a far reaching influence on the design itself. These influences are investigated in this paper by adapting a systolic array of CORDIC devices to be implemented adiabatically. We present a means to describe adiabatic logic in VHDL and use it to define the systolic array with precise timing and bit-true calculations. The large pipeline bubbles that occur in a naive version of this array are identified and removed to a large degree. As an example, we demonstrate a parameterization of the CORDIC array that carries out adaptive RLS filtering.
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46

Dai, Jing, Rui Zhou, and Shi Tang. "Design Method of DLMS Adaptive Filter Based on FPGA." Applied Mechanics and Materials 182-183 (June 2012): 685–89. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.685.

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This paper proposed DLMS algorithm which is suitable for real-time signal processing. The algorithm adopts hardware multiplier and pipeline technology to accelerate the operation of the system. By analyzing the time sequence and spectrum of the output signals, the attenuation of the interference signals is about 51dB. It can further demonstrate the high quality and high speed of the DLMS algorithm to filter out the interference signals, and well handle the relationship between the resources and speed of FPGA to meet the requirements of high-speed signal processing.
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47

Martinek, Radek, Martina Ladrova, Michaela Sidikova, Rene Jaros, Khosrow Behbehani, Radana Kahankova, and Aleksandra Kawala-Sterniuk. "Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals." Sensors 21, no. 15 (July 30, 2021): 5186. http://dx.doi.org/10.3390/s21155186.

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Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today’s clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.
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48

Saeed, Amer T., Zaid Raad Saber, Ahmed M. Sana, and Musa A. Hameed. "Eliminating unwanted signals in sound by using digital signal processing system." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (May 1, 2020): 829. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp829-834.

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<p><a name="_Hlk536186602"></a><span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Unwanted signals or noise signals in sound files are considered one of the major challenges and issues for a thousand users. It is impossible to reduce or remove these noise signals without identifying their types and ranges. Therefore, to address one of the big problems in the digital or analogue communication, which is noise signals or unwanted signals, an adaptive selection method and noise signal removal algorithm are proposed in this research. The proposed algorithm is done through specifying the types of undesirable signals, frequency, and time range, then utilizing digital signal processing system which includes design several types of digital filters based on the types and numbers of unwanted signals. Four digital filters are used in this research to remove noise signals from the sound file by implementing the proposed algorithm using Matlab Code. Results show that our proposed algorithm was done successfully and the whole noise signals were removed without any negative consequence in the output sound signal. </span><span style="font-family: 'Times New Roman', serif; font-size: 9pt;">Unwanted signals or noise signals in sound files are considered one of the major challenges and issues for a thousand users. It is impossible to reduce or remove these noise signals without identifying their types and ranges. Therefore, to address one of the big problems in the digital or analogue communication, which is noise signals or unwanted signals, an adaptive selection method and noise signal removal algorithm are proposed in this research. The proposed algorithm is done through specifying the types of undesirable signals, frequency, and time range, then utilizing digital signal processing system which includes design several types of digital filters based on the types and numbers of unwanted signals. Four digital filters are used in this research to remove noise signals from the sound file by implementing the proposed algorithm using Matlab Code. Results show that our proposed algorithm was done successfully and the whole noise signals were removed without any negative consequence in the output sound signal.</span></p>
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49

Silong Peng and Wen-Liang Hwang. "Adaptive Signal Decomposition Based on Local Narrow Band Signals." IEEE Transactions on Signal Processing 56, no. 7 (July 2008): 2669–76. http://dx.doi.org/10.1109/tsp.2008.917360.

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50

Yang, Dan, Bin Xu, Lin Lin Ye, and Xu Wang. "Speech Enhancement Using Wavelet Neural Network with Sub-Band Adaptive Matched Filter." Advanced Engineering Forum 2-3 (December 2011): 127–30. http://dx.doi.org/10.4028/www.scientific.net/aef.2-3.127.

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Wavelet Neural Network (WNN) Is a Time-frequency Analysis Method, which Detects the Subtle Small Changes in the Signal Frequency Domain. Adaptive Filter Provides a Kind of Simple and Applied Method for Processing Signals in Noise. in this Paper, we Proposed a New Speech Enhancement Technique which Is Based on Wavelet Neural Network Using Adaptive Matched Filter Adjusting Weight. we Choose the Signal with Noise Pollution as the Input Signal and then Put it to the Trained Wavelet Neural Network. Wavelet Decomposition and Wavelet Neural Network Weights Processing Adopt Signal Sub-band Adaptive Matched Filter, the Output Signal of Wavelet Neural Network Is an Approximation Form of Original Signal. the Results Show that the WNN Is a Quite Effective Method for the Speech Enhancement and Improving the Ration of Signal to Noise.
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