Digital signal processing - Revision history

Kvng: Reverted good faith edits by Cloud899 (talk): Maybe add to body first. seems too specific for lead.

2025-05-20T18:48:35Z

Reverted good faith edits by Cloud899 (talk): Maybe add to body first. seems too specific for lead.

← Previous revision		Revision as of 18:48, 20 May 2025
Line 88:		Line 88:
	{{Div col end}}		{{Div col end}}

	Specific examples include [[speech coding]] and transmission in digital [[mobile phone]]s, [[room correction]] of sound in [[hi-fi]] and [[sound reinforcement]] applications, analysis and control of [[industrial process]]es, [[medical imaging]] such as [[Computed axial tomography\|CAT]] scans and [[MRI]], [[audio crossover]]s and [[equalization (audio)\|equalization]], [[digital synthesizer]]s, audio [[effects unit]]s ~~and mobile audio surveillance platforms such as Hypatia, a real-time encrypted emergency response application~~.<ref>{{Cite web \|last=Chidester \|first=Ashlan \|date=2025 \|title=Hypatia – Advanced Mobile Security and Emergency Response Software \|url=https://osf.io/c5gnj/ \|website=OSF \|publisher=Center for Open Science}}</ref><ref>{{cite book \|last1=Rabiner \|first1=Lawrence R. \|author1-link=Lawrence Rabiner \|last2=Gold \|first2=Bernard \|date=1975 \|title=Theory and application of digital signal processing \|location=Englewood Cliffs, NJ \|publisher=Prentice-Hall, Inc. \|isbn=978-0139141010 \|url-access=registration \|url=https://archive.org/details/theoryapplicatio00rabi }}</ref> DSP has been used in [[hearing aid]] technology since 1996, which allows for automatic directional microphones, complex digital [[noise reduction]], and improved adjustment of the [[frequency response]].<ref>{{Cite journal \|last=Kerckhoff \|first=Jessica \|last2=Listenberger \|first2=Jennifer \|last3=Valente \|first3=Michael \|date=October 1, 2008 \|title=Advances in hearing aid technology \|url=https://digitalcommons.wustl.edu/audio_hapubs/28 \|journal=Contemporary Issues in Communication Science and Disorders \|volume=35 \|pages=102–112 \|doi=10.1044/cicsd_35_F_102}}</ref>		Specific examples include [[speech coding]] and transmission in digital [[mobile phone]]s, [[room correction]] of sound in [[hi-fi]] and [[sound reinforcement]] applications, analysis and control of [[industrial process]]es, [[medical imaging]] such as [[Computed axial tomography\|CAT]] scans and [[MRI]], [[audio crossover]]s and [[equalization (audio)\|equalization]], [[digital synthesizer]]s, and audio [[effects unit]]s.<ref>{{cite book \|last1=Rabiner \|first1=Lawrence R. \|author1-link=Lawrence Rabiner \|last2=Gold \|first2=Bernard \|date=1975 \|title=Theory and application of digital signal processing \|location=Englewood Cliffs, NJ \|publisher=Prentice-Hall, Inc. \|isbn=978-0139141010 \|url-access=registration \|url=https://archive.org/details/theoryapplicatio00rabi }}</ref> DSP has been used in [[hearing aid]] technology since 1996, which allows for automatic directional microphones, complex digital [[noise reduction]], and improved adjustment of the [[frequency response]].<ref>{{Cite journal \|last=Kerckhoff \|first=Jessica \|last2=Listenberger \|first2=Jennifer \|last3=Valente \|first3=Michael \|date=October 1, 2008 \|title=Advances in hearing aid technology \|url=https://digitalcommons.wustl.edu/audio_hapubs/28 \|journal=Contemporary Issues in Communication Science and Disorders \|volume=35 \|pages=102–112 \|doi=10.1044/cicsd_35_F_102}}</ref>

	== Techniques ==		== Techniques ==

Cloud899: /* Applications */

2025-05-17T21:30:58Z

Applications

← Previous revision		Revision as of 21:30, 17 May 2025
Line 88:		Line 88:
	{{Div col end}}		{{Div col end}}

	Specific examples include [[speech coding]] and transmission in digital [[mobile phone]]s, [[room correction]] of sound in [[hi-fi]] and [[sound reinforcement]] applications, analysis and control of [[industrial process]]es, [[medical imaging]] such as [[Computed axial tomography\|CAT]] scans and [[MRI]], [[audio crossover]]s and [[equalization (audio)\|equalization]], [[digital synthesizer]]s, ~~and~~ audio [[effects unit]]s.<ref>{{cite book \|last1=Rabiner \|first1=Lawrence R. \|author1-link=Lawrence Rabiner \|last2=Gold \|first2=Bernard \|date=1975 \|title=Theory and application of digital signal processing \|location=Englewood Cliffs, NJ \|publisher=Prentice-Hall, Inc. \|isbn=978-0139141010 \|url-access=registration \|url=https://archive.org/details/theoryapplicatio00rabi }}</ref> DSP has been used in [[hearing aid]] technology since 1996, which allows for automatic directional microphones, complex digital [[noise reduction]], and improved adjustment of the [[frequency response]].<ref>{{Cite journal \|last=Kerckhoff \|first=Jessica \|last2=Listenberger \|first2=Jennifer \|last3=Valente \|first3=Michael \|date=October 1, 2008 \|title=Advances in hearing aid technology \|url=https://digitalcommons.wustl.edu/audio_hapubs/28 \|journal=Contemporary Issues in Communication Science and Disorders \|volume=35 \|pages=102–112 \|doi=10.1044/cicsd_35_F_102}}</ref>		Specific examples include [[speech coding]] and transmission in digital [[mobile phone]]s, [[room correction]] of sound in [[hi-fi]] and [[sound reinforcement]] applications, analysis and control of [[industrial process]]es, [[medical imaging]] such as [[Computed axial tomography\|CAT]] scans and [[MRI]], [[audio crossover]]s and [[equalization (audio)\|equalization]], [[digital synthesizer]]s, audio [[effects unit]]s and mobile audio surveillance platforms such as Hypatia, a real-time encrypted emergency response application.<ref>{{Cite web \|last=Chidester \|first=Ashlan \|date=2025 \|title=Hypatia – Advanced Mobile Security and Emergency Response Software \|url=https://osf.io/c5gnj/ \|website=OSF \|publisher=Center for Open Science}}</ref><ref>{{cite book \|last1=Rabiner \|first1=Lawrence R. \|author1-link=Lawrence Rabiner \|last2=Gold \|first2=Bernard \|date=1975 \|title=Theory and application of digital signal processing \|location=Englewood Cliffs, NJ \|publisher=Prentice-Hall, Inc. \|isbn=978-0139141010 \|url-access=registration \|url=https://archive.org/details/theoryapplicatio00rabi }}</ref> DSP has been used in [[hearing aid]] technology since 1996, which allows for automatic directional microphones, complex digital [[noise reduction]], and improved adjustment of the [[frequency response]].<ref>{{Cite journal \|last=Kerckhoff \|first=Jessica \|last2=Listenberger \|first2=Jennifer \|last3=Valente \|first3=Michael \|date=October 1, 2008 \|title=Advances in hearing aid technology \|url=https://digitalcommons.wustl.edu/audio_hapubs/28 \|journal=Contemporary Issues in Communication Science and Disorders \|volume=35 \|pages=102–112 \|doi=10.1044/cicsd_35_F_102}}</ref>

	== Techniques ==		== Techniques ==

Kvng: grammar. tag engvar.

2025-01-05T18:08:21Z

grammar. tag engvar.

← Previous revision		Revision as of 18:08, 5 January 2025
Line 1:		Line 1:
	{{short description\|Mathematical signal manipulation by computers}}		{{short description\|Mathematical signal manipulation by computers}}
	{{Redirect\|Digital transform\|the impact of digital technology on society\|Digital transformation}}		{{Redirect\|Digital transform\|the impact of digital technology on society\|Digital transformation}}

	{{More citations needed\|date=May 2008}}		{{More citations needed\|date=May 2008}}
			{{Use American English\|date=January 2025}}

	'''Digital signal processing''' ('''DSP''') is the use of [[digital processing]], such as by computers or more specialized [[digital signal processor]]s, to perform a wide variety of [[signal processing]] operations. The [[digital signal]]s processed in this manner are a sequence of numbers that represent [[Sampling (signal processing)\|samples]] of a [[continuous variable]] in a domain such as time, space, or frequency. In [[digital electronics]], a digital signal is represented as a [[pulse train]],<ref>{{cite book \|author=B. SOMANATHAN NAIR \|title=Digital electronics and logic design \|date=2002 \|isbn=9788120319561 \|publisher=PHI Learning Pvt. Ltd. \|quote=Digital signals are fixed-width pulses, which occupy only one of two levels of amplitude. \|page=289}}</ref><ref>{{cite book \|author=Joseph Migga Kizza \|isbn=9780387204734 \|date=2005 \|publisher=Springer Science & Business Media \|title=Computer Network Security}}</ref> which is typically generated by the switching of a [[transistor]].<ref>{{cite book \|title=2000 Solved Problems in Digital Electronics \|date=2005 \|publisher=[[Tata McGraw-Hill Education]] \|isbn=978-0-07-058831-8 \|page=151 \|url=https://books.google.com/books?id=N6FDii6_nSEC&pg=PA151}}</ref>		'''Digital signal processing''' ('''DSP''') is the use of [[digital processing]], such as by computers or more specialized [[digital signal processor]]s, to perform a wide variety of [[signal processing]] operations. The [[digital signal]]s processed in this manner are a sequence of numbers that represent [[Sampling (signal processing)\|samples]] of a [[continuous variable]] in a domain such as time, space, or frequency. In [[digital electronics]], a digital signal is represented as a [[pulse train]],<ref>{{cite book \|author=B. SOMANATHAN NAIR \|title=Digital electronics and logic design \|date=2002 \|isbn=9788120319561 \|publisher=PHI Learning Pvt. Ltd. \|quote=Digital signals are fixed-width pulses, which occupy only one of two levels of amplitude. \|page=289}}</ref><ref>{{cite book \|author=Joseph Migga Kizza \|isbn=9780387204734 \|date=2005 \|publisher=Springer Science & Business Media \|title=Computer Network Security}}</ref> which is typically generated by the switching of a [[transistor]].<ref>{{cite book \|title=2000 Solved Problems in Digital Electronics \|date=2005 \|publisher=[[Tata McGraw-Hill Education]] \|isbn=978-0-07-058831-8 \|page=151 \|url=https://books.google.com/books?id=N6FDii6_nSEC&pg=PA151}}</ref>
Line 54:		Line 54:

	===Empirical mode decomposition===		===Empirical mode decomposition===
	Empirical mode decomposition is based on decomposition signal into [[intrinsic mode function]]s (IMFs). IMFs are ~~quasiharmonical~~ oscillations that are extracted from the signal.<ref>{{Cite journal\| doi = 10.1098/rspa.1998.0193\| issn = 1364-5021\| volume = 454\| issue = 1971\| pages = 903–995\| last1 = Huang\| first1 = N. E.\| last2 = Shen\| first2 = Z.\| last3 = Long\| first3 = S. R.\| last4 = Wu\| first4 = M. C.\| last5 = Shih\| first5 = H. H.\| last6 = Zheng\| first6 = Q.\| last7 = Yen\| first7 = N.-C.\| last8 = Tung\| first8 = C. C.\| last9 = Liu\| first9 = H. H.\| title = The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis\| journal = Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences\| access-date = 2018-06-05\| date = 1998-03-08\| bibcode = 1998RSPSA.454..903H\| s2cid = 1262186\| url = http://rspa.royalsocietypublishing.org/cgi/doi/10.1098/rspa.1998.0193}}</ref>		Empirical mode decomposition is based on decomposition signal into [[intrinsic mode function]]s (IMFs). IMFs are quasi-harmonical oscillations that are extracted from the signal.<ref>{{Cite journal\| doi = 10.1098/rspa.1998.0193\| issn = 1364-5021\| volume = 454\| issue = 1971\| pages = 903–995\| last1 = Huang\| first1 = N. E.\| last2 = Shen\| first2 = Z.\| last3 = Long\| first3 = S. R.\| last4 = Wu\| first4 = M. C.\| last5 = Shih\| first5 = H. H.\| last6 = Zheng\| first6 = Q.\| last7 = Yen\| first7 = N.-C.\| last8 = Tung\| first8 = C. C.\| last9 = Liu\| first9 = H. H.\| title = The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis\| journal = Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences\| access-date = 2018-06-05\| date = 1998-03-08\| bibcode = 1998RSPSA.454..903H\| s2cid = 1262186\| url = http://rspa.royalsocietypublishing.org/cgi/doi/10.1098/rspa.1998.0193}}</ref>

	== Implementation ==		== Implementation ==
	DSP [[algorithm]]s may be run on general-purpose computers<ref>{{Cite book \|last1=Weipeng \|first1=Jiang \|last2=Zhiqiang \|first2=He \|last3=Ran \|first3=Duan \|last4=Xinglin \|first4=Wang \|title=7th International Conference on Communications and Networking in China \|chapter=Major optimization methods for TD-LTE signal processing based on general purpose processor \|date=August 2012 \|chapter-url=https://ieeexplore.ieee.org/document/6417593 \|pages=797–801 \|doi=10.1109/ChinaCom.2012.6417593\|isbn=978-1-4673-2699-5 \|s2cid=17594911 }}</ref> and [[digital signal processor]]s.<ref>{{Cite book \|last1=Zaynidinov \|first1=Hakimjon \|last2=Ibragimov \|first2=Sanjarbek \|last3=Tojiboyev \|first3=Gayrat \|last4=Nurmurodov \|first4=Javohir \|chapter=Efficiency of Parallelization of Haar Fast Transform Algorithm in Dual-Core Digital Signal Processors \|date=2021-06-22 \|title=2021 8th International Conference on Computer and Communication Engineering (ICCCE) \|url=https://ieeexplore.ieee.org/document/9467190 \|publisher=IEEE \|pages=7–12 \|doi=10.1109/ICCCE50029.2021.9467190 \|isbn=978-1-7281-1065-3\|s2cid=236187914 }}</ref> DSP algorithms are also implemented on purpose-built hardware such as [[application-specific integrated circuit]] (ASICs).<ref>{{Cite journal \|last=Lyakhov \|first=P.A. \|date=June 2023 \|title=Area-Efficient digital filtering based on truncated multiply-accumulate units in residue number system 2 n - 1 , 2 n , 2 n + 1 \|journal=Journal of King Saud University - Computer and Information Sciences \|language=en \|volume=35 \|issue=6 \|pages=101574 \|doi=10.1016/j.jksuci.2023.101574\|doi-access=free }}</ref> Additional technologies for digital signal processing include more powerful general purpose [[microprocessor]]s, [[graphics processing unit]]s, [[field-programmable gate array]]s (FPGAs), [[digital signal controller]]s (mostly for industrial applications such as motor control), and [[stream processing\|stream processors]].<ref>{{cite book \|title=Digital Signal Processing and Applications \|last1=Stranneby \|first1=Dag \|last2=Walker \|first2=William \|edition=2nd \|publisher=Elsevier \|year=2004 \|isbn=0-7506-6344-8 \|url=https://books.google.com/books?id=NKK1DdqcDVUC&pg=PA241}}</ref>		DSP [[algorithm]]s may be run on general-purpose computers<ref>{{Cite book \|last1=Weipeng \|first1=Jiang \|last2=Zhiqiang \|first2=He \|last3=Ran \|first3=Duan \|last4=Xinglin \|first4=Wang \|title=7th International Conference on Communications and Networking in China \|chapter=Major optimization methods for TD-LTE signal processing based on general purpose processor \|date=August 2012 \|chapter-url=https://ieeexplore.ieee.org/document/6417593 \|pages=797–801 \|doi=10.1109/ChinaCom.2012.6417593\|isbn=978-1-4673-2699-5 \|s2cid=17594911 }}</ref> and [[digital signal processor]]s.<ref>{{Cite book \|last1=Zaynidinov \|first1=Hakimjon \|last2=Ibragimov \|first2=Sanjarbek \|last3=Tojiboyev \|first3=Gayrat \|last4=Nurmurodov \|first4=Javohir \|chapter=Efficiency of Parallelization of Haar Fast Transform Algorithm in Dual-Core Digital Signal Processors \|date=2021-06-22 \|title=2021 8th International Conference on Computer and Communication Engineering (ICCCE) \|url=https://ieeexplore.ieee.org/document/9467190 \|publisher=IEEE \|pages=7–12 \|doi=10.1109/ICCCE50029.2021.9467190 \|isbn=978-1-7281-1065-3\|s2cid=236187914 }}</ref> DSP algorithms are also implemented on purpose-built hardware such as [[application-specific integrated circuit]] (ASICs).<ref>{{Cite journal \|last=Lyakhov \|first=P.A. \|date=June 2023 \|title=Area-Efficient digital filtering based on truncated multiply-accumulate units in residue number system 2 n - 1 , 2 n , 2 n + 1 \|journal=Journal of King Saud University - Computer and Information Sciences \|language=en \|volume=35 \|issue=6 \|pages=101574 \|doi=10.1016/j.jksuci.2023.101574\|doi-access=free }}</ref> Additional technologies for digital signal processing include more powerful general-purpose [[microprocessor]]s, [[graphics processing unit]]s, [[field-programmable gate array]]s (FPGAs), [[digital signal controller]]s (mostly for industrial applications such as motor control), and [[stream processing\|stream processors]].<ref>{{cite book \|title=Digital Signal Processing and Applications \|last1=Stranneby \|first1=Dag \|last2=Walker \|first2=William \|edition=2nd \|publisher=Elsevier \|year=2004 \|isbn=0-7506-6344-8 \|url=https://books.google.com/books?id=NKK1DdqcDVUC&pg=PA241}}</ref>

	For systems that do not have a [[real-time computing]] requirement and the signal data (either input or output) exists in data files, processing may be done economically with a general-purpose computer. This is essentially no different from any other [[data processing]], except DSP mathematical techniques (such as the [[Discrete cosine transform\|DCT]] and [[FFT]]) are used, and the sampled data is usually assumed to be uniformly sampled in time or space. An example of such an application is processing [[digital photograph]]s with software such as [[Photoshop]].		For systems that do not have a [[real-time computing]] requirement and the signal data (either input or output) exists in data files, processing may be done economically with a general-purpose computer. This is essentially no different from any other [[data processing]], except DSP mathematical techniques (such as the [[Discrete cosine transform\|DCT]] and [[FFT]]) are used, and the sampled data is usually assumed to be uniformly sampled in time or space. An example of such an application is processing [[digital photograph]]s with software such as [[Photoshop]].
Line 63:		Line 63:
	When the application requirement is real-time, DSP is often implemented using specialized or dedicated processors or microprocessors, sometimes using multiple processors or multiple processing cores. These may process data using fixed-point arithmetic or floating point. For more demanding applications [[FPGA]]s may be used.<ref>{{cite web \|last=JPFix \|title=FPGA-Based Image Processing Accelerator \|url=http://www.jpfix.com/About_Us/Articles/FPGA-Based_Image_Processing_Ac/fpga-based_image_processing_ac.html \|date=2006 \|access-date=2008-05-10}}</ref> For the most demanding applications or high-volume products, [[ASIC]]s might be designed specifically for the application.		When the application requirement is real-time, DSP is often implemented using specialized or dedicated processors or microprocessors, sometimes using multiple processors or multiple processing cores. These may process data using fixed-point arithmetic or floating point. For more demanding applications [[FPGA]]s may be used.<ref>{{cite web \|last=JPFix \|title=FPGA-Based Image Processing Accelerator \|url=http://www.jpfix.com/About_Us/Articles/FPGA-Based_Image_Processing_Ac/fpga-based_image_processing_ac.html \|date=2006 \|access-date=2008-05-10}}</ref> For the most demanding applications or high-volume products, [[ASIC]]s might be designed specifically for the application.

	Parallel implementations of DSP algorithms, ~~utilising~~ multi-core CPU and many-core GPU architectures, are developed to improve the performances in terms of latency of these algorithms.<ref name=":0">{{Cite book \|last1=Kapinchev \|first1=Konstantin \|last2=Bradu \|first2=Adrian \|last3=Podoleanu \|first3=Adrian \|title=2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS) \|chapter=Parallel Approaches to Digital Signal Processing Algorithms with Applications in Medical Imaging \|date=December 2019 \|chapter-url=https://ieeexplore.ieee.org/document/9008720 \|pages=1–7 \|doi=10.1109/ICSPCS47537.2019.9008720\|isbn=978-1-7281-2194-9 \|s2cid=211686462 \|url=https://kar.kent.ac.uk/80930/1/Kapinchev2019.pdf }}</ref>		Parallel implementations of DSP algorithms, utilizing multi-core CPU and many-core GPU architectures, are developed to improve the performances in terms of latency of these algorithms.<ref name=":0">{{Cite book \|last1=Kapinchev \|first1=Konstantin \|last2=Bradu \|first2=Adrian \|last3=Podoleanu \|first3=Adrian \|title=2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS) \|chapter=Parallel Approaches to Digital Signal Processing Algorithms with Applications in Medical Imaging \|date=December 2019 \|chapter-url=https://ieeexplore.ieee.org/document/9008720 \|pages=1–7 \|doi=10.1109/ICSPCS47537.2019.9008720\|isbn=978-1-7281-2194-9 \|s2cid=211686462 \|url=https://kar.kent.ac.uk/80930/1/Kapinchev2019.pdf }}</ref>

	'''{{vanchor\|Native processing}}''' is done by the computer's CPU rather than by DSP or outboard processing, which is done by additional third-party DSP chips located on extension cards or external hardware boxes or racks. Many [[digital audio workstation]]s such as [[Logic Pro]], [[Cubase]], [[Digital Performer]] and [[Pro Tools]] LE use native processing. Others, such as [[Pro Tools]] HD, [[Universal Audio (company)\|Universal Audio]]'s UAD-1 and [[TC Electronic]]'s Powercore use DSP processing.		'''{{vanchor\|Native processing}}''' is done by the computer's CPU rather than by DSP or outboard processing, which is done by additional third-party DSP chips located on extension cards or external hardware boxes or racks. Many [[digital audio workstation]]s such as [[Logic Pro]], [[Cubase]], [[Digital Performer]] and [[Pro Tools]] LE use native processing. Others, such as [[Pro Tools]] HD, [[Universal Audio (company)\|Universal Audio]]'s UAD-1 and [[TC Electronic]]'s Powercore use DSP processing.
Line 124:		Line 124:
	* [[Wavelet]]		* [[Wavelet]]
	{{Div col end}}		{{Div col end}}

	== Further reading ==		== Further reading ==
	{{wikibooks\|Digital Signal Processing}}		{{wikibooks\|Digital Signal Processing}}

Ca: /* Software Tools for Digital Signal Processing */ remove AI

2024-12-08T13:46:56Z

Software Tools for Digital Signal Processing: remove AI

@@ Line 124: / Line 124: @@
 * [[Wavelet]]
 {{Div col end}}
 == Further reading ==
 {{wikibooks|Digital Signal Processing}}

Kvng: mv new contribution to Noise reduction

2024-11-17T17:27:23Z

mv new contribution to Noise reduction

@@ Line 52: / Line 52: @@
 [[File:Jpeg2000 2-level wavelet transform-lichtenstein.png|thumb|300px|An example of the 2D discrete wavelet transform that is used in [[JPEG2000]]. The original image is high-pass filtered, yielding the three large images, each describing local changes in brightness (details) in the original image. It is then low-pass filtered and downscaled, yielding an approximation image; this image is high-pass filtered to produce the three smaller detail images, and low-pass filtered to produce the final approximation image in the upper-left.]]
 In [[numerical analysis]] and [[functional analysis]], a [[discrete wavelet transform]] is any [[wavelet transform]] for which the [[wavelet]]s are discretely sampled. As with other wavelet transforms, a key advantage it has over [[Fourier transform]]s is temporal resolution: it captures both frequency ''and'' location information. The accuracy of the joint time-frequency resolution is limited by the [[Uncertainty principle#Signal processing|uncertainty principle]] of time-frequency.
 ===Empirical mode decomposition===

Arav 52: /* Wavelet */

2024-11-14T09:53:09Z

Wavelet

← Previous revision		Revision as of 09:53, 14 November 2024
Line 59:		Line 59:
	Noise reduction techniques in Digital Signal Processing (DSP) are essential for improving the quality of signals in various applications, including audio processing, telecommunications, and biomedical engineering. Noise, which is unwanted random variation in signals, can degrade signal clarity and accuracy. DSP offers a range of algorithms to reduce noise while preserving the integrity of the original signal.		Noise reduction techniques in Digital Signal Processing (DSP) are essential for improving the quality of signals in various applications, including audio processing, telecommunications, and biomedical engineering. Noise, which is unwanted random variation in signals, can degrade signal clarity and accuracy. DSP offers a range of algorithms to reduce noise while preserving the integrity of the original signal.

	1.Spectral Subtraction		'''1.Spectral Subtraction:'''

	Spectral subtraction is one of the simplest and most widely used noise reduction techniques, especially in speech processing. It works by estimating the power spectrum of the noise during silent periods and subtracting this noise spectrum from the noisy signal. This technique assumes that noise is additive and relatively stationary. While effective, spectral subtraction can introduce "musical noise," a type of artificial noise, if the noise spectrum estimate is inaccurate.		Spectral subtraction is one of the simplest and most widely used noise reduction techniques, especially in speech processing. It works by estimating the power spectrum of the noise during silent periods and subtracting this noise spectrum from the noisy signal. This technique assumes that noise is additive and relatively stationary. While effective, spectral subtraction can introduce "musical noise," a type of artificial noise, if the noise spectrum estimate is inaccurate.

Line 68:		Line 69:
	Limitations: Tends to perform poorly in the presence of non-stationary noise, and can introduce artifacts.		Limitations: Tends to perform poorly in the presence of non-stationary noise, and can introduce artifacts.

	2. Adaptive Filtering		'''2. Adaptive Filtering:'''

	Adaptive filters are highly effective in situations where noise is unpredictable or non-stationary. In adaptive filtering, the filter's parameters are continuously adjusted to minimize the difference between the desired signal and the actual output. The Least Mean Squares (LMS) and Recursive Least Squares (RLS) algorithms are commonly used for adaptive noise cancellation.		Adaptive filters are highly effective in situations where noise is unpredictable or non-stationary. In adaptive filtering, the filter's parameters are continuously adjusted to minimize the difference between the desired signal and the actual output. The Least Mean Squares (LMS) and Recursive Least Squares (RLS) algorithms are commonly used for adaptive noise cancellation.

Line 77:		Line 79:
	Limitations: Higher computational requirements, which may be challenging for real-time applications on low-power devices.		Limitations: Higher computational requirements, which may be challenging for real-time applications on low-power devices.

	3. Wiener Filtering		'''3. Wiener Filtering:'''

	Wiener filtering is a statistical approach to noise reduction that minimizes the mean square error between the desired signal and the actual output. This technique relies on knowledge of both the signal and noise power spectra, and it can provide optimal noise reduction if these spectra are accurately estimated.		Wiener filtering is a statistical approach to noise reduction that minimizes the mean square error between the desired signal and the actual output. This technique relies on knowledge of both the signal and noise power spectra, and it can provide optimal noise reduction if these spectra are accurately estimated.

Line 86:		Line 89:
	Limitations: Requires accurate estimates of the signal and noise statistics, which may not always be feasible in real-world applications.		Limitations: Requires accurate estimates of the signal and noise statistics, which may not always be feasible in real-world applications.

	4. Kalman Filtering		'''4. Kalman Filtering:'''

	Kalman filtering is a recursive algorithm that estimates the state of a dynamic system from a series of noisy measurements. While typically used for tracking and prediction, it is also applicable to noise reduction, especially for signals that can be modeled as time-varying. Kalman filtering is particularly effective in applications where the signal is dynamic and the noise characteristics vary over time.		Kalman filtering is a recursive algorithm that estimates the state of a dynamic system from a series of noisy measurements. While typically used for tracking and prediction, it is also applicable to noise reduction, especially for signals that can be modeled as time-varying. Kalman filtering is particularly effective in applications where the signal is dynamic and the noise characteristics vary over time.

Line 95:		Line 99:
	Limitations: Requires a mathematical model of the system dynamics, which may be complex to design for certain applications.		Limitations: Requires a mathematical model of the system dynamics, which may be complex to design for certain applications.

	5. Wavelet-Based Denoising		'''5. Wavelet-Based Denoising:'''

	Wavelet-based denoising (or wavelet thresholding) decomposes the signal into different frequency components using a wavelet transform and then removes the noise by thresholding the wavelet coefficients. This method is effective for signals with sharp transients, like biomedical signals, because wavelet transforms can provide both time and frequency information.		Wavelet-based denoising (or wavelet thresholding) decomposes the signal into different frequency components using a wavelet transform and then removes the noise by thresholding the wavelet coefficients. This method is effective for signals with sharp transients, like biomedical signals, because wavelet transforms can provide both time and frequency information.

Line 104:		Line 109:
	Limitations: The choice of wavelet basis and thresholding parameters significantly impacts performance, requiring careful tuning.		Limitations: The choice of wavelet basis and thresholding parameters significantly impacts performance, requiring careful tuning.

	6. Non-Local Means (NLM) Denoising		'''6. Non-Local Means (NLM) Denoising:'''

	Non-Local Means is an advanced noise reduction technique that uses redundancy in the signal by averaging similar patches across the signal or image. While computationally more demanding, NLM is highly effective in removing noise from images and audio signals without blurring.		Non-Local Means is an advanced noise reduction technique that uses redundancy in the signal by averaging similar patches across the signal or image. While computationally more demanding, NLM is highly effective in removing noise from images and audio signals without blurring.

Arav 52 at 09:50, 14 November 2024

2024-11-14T09:50:11Z

← Previous revision		Revision as of 09:50, 14 November 2024
Line 52:		Line 52:
	[[File:Jpeg2000 2-level wavelet transform-lichtenstein.png\|thumb\|300px\|An example of the 2D discrete wavelet transform that is used in [[JPEG2000]]. The original image is high-pass filtered, yielding the three large images, each describing local changes in brightness (details) in the original image. It is then low-pass filtered and downscaled, yielding an approximation image; this image is high-pass filtered to produce the three smaller detail images, and low-pass filtered to produce the final approximation image in the upper-left.]]		[[File:Jpeg2000 2-level wavelet transform-lichtenstein.png\|thumb\|300px\|An example of the 2D discrete wavelet transform that is used in [[JPEG2000]]. The original image is high-pass filtered, yielding the three large images, each describing local changes in brightness (details) in the original image. It is then low-pass filtered and downscaled, yielding an approximation image; this image is high-pass filtered to produce the three smaller detail images, and low-pass filtered to produce the final approximation image in the upper-left.]]
	In [[numerical analysis]] and [[functional analysis]], a [[discrete wavelet transform]] is any [[wavelet transform]] for which the [[wavelet]]s are discretely sampled. As with other wavelet transforms, a key advantage it has over [[Fourier transform]]s is temporal resolution: it captures both frequency ''and'' location information. The accuracy of the joint time-frequency resolution is limited by the [[Uncertainty principle#Signal processing\|uncertainty principle]] of time-frequency.		In [[numerical analysis]] and [[functional analysis]], a [[discrete wavelet transform]] is any [[wavelet transform]] for which the [[wavelet]]s are discretely sampled. As with other wavelet transforms, a key advantage it has over [[Fourier transform]]s is temporal resolution: it captures both frequency ''and'' location information. The accuracy of the joint time-frequency resolution is limited by the [[Uncertainty principle#Signal processing\|uncertainty principle]] of time-frequency.



	'''Noise Reduction Techniques in Digital Signal Processing'''		'''Noise Reduction Techniques in Digital Signal Processing'''

Arav 52: /* Wavelet */

2024-11-14T09:49:31Z

Wavelet

← Previous revision		Revision as of 09:49, 14 November 2024
Line 53:		Line 53:
	In [[numerical analysis]] and [[functional analysis]], a [[discrete wavelet transform]] is any [[wavelet transform]] for which the [[wavelet]]s are discretely sampled. As with other wavelet transforms, a key advantage it has over [[Fourier transform]]s is temporal resolution: it captures both frequency ''and'' location information. The accuracy of the joint time-frequency resolution is limited by the [[Uncertainty principle#Signal processing\|uncertainty principle]] of time-frequency.		In [[numerical analysis]] and [[functional analysis]], a [[discrete wavelet transform]] is any [[wavelet transform]] for which the [[wavelet]]s are discretely sampled. As with other wavelet transforms, a key advantage it has over [[Fourier transform]]s is temporal resolution: it captures both frequency ''and'' location information. The accuracy of the joint time-frequency resolution is limited by the [[Uncertainty principle#Signal processing\|uncertainty principle]] of time-frequency.

	Noise Reduction Techniques in Digital Signal Processing		'''Noise Reduction Techniques in Digital Signal Processing'''

	Noise reduction techniques in Digital Signal Processing (DSP) are essential for improving the quality of signals in various applications, including audio processing, telecommunications, and biomedical engineering. Noise, which is unwanted random variation in signals, can degrade signal clarity and accuracy. DSP offers a range of algorithms to reduce noise while preserving the integrity of the original signal.		Noise reduction techniques in Digital Signal Processing (DSP) are essential for improving the quality of signals in various applications, including audio processing, telecommunications, and biomedical engineering. Noise, which is unwanted random variation in signals, can degrade signal clarity and accuracy. DSP offers a range of algorithms to reduce noise while preserving the integrity of the original signal.

Arav 52: /* Wavelet */

2024-11-14T09:48:25Z

Wavelet

@@ Line 52: / Line 52: @@
 [[File:Jpeg2000 2-level wavelet transform-lichtenstein.png|thumb|300px|An example of the 2D discrete wavelet transform that is used in [[JPEG2000]]. The original image is high-pass filtered, yielding the three large images, each describing local changes in brightness (details) in the original image. It is then low-pass filtered and downscaled, yielding an approximation image; this image is high-pass filtered to produce the three smaller detail images, and low-pass filtered to produce the final approximation image in the upper-left.]]
 In [[numerical analysis]] and [[functional analysis]], a [[discrete wavelet transform]] is any [[wavelet transform]] for which the [[wavelet]]s are discretely sampled. As with other wavelet transforms, a key advantage it has over [[Fourier transform]]s is temporal resolution: it captures both frequency ''and'' location information. The accuracy of the joint time-frequency resolution is limited by the [[Uncertainty principle#Signal processing|uncertainty principle]] of time-frequency.
 ===Empirical mode decomposition===

Aditya.Jadhav22 at 10:56, 22 October 2024

2024-10-22T10:56:08Z

← Previous revision		Revision as of 10:56, 22 October 2024
Line 256:		Line 256:
	[[Category:Telecommunication theory]]		[[Category:Telecommunication theory]]
	[[Category:Radar signal processing]]		[[Category:Radar signal processing]]
	`1