Signal processing: Difference between revisions

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Analog signal processing is for signals that have not been digitized, as in most 20th-century [[radio]], telephone, and television systems. This involves linear electronic circuits as well as nonlinear ones. The former are, for instance, [[passive filter]]s, [[active filter]]s, [[Electronic mixer|additive mixers]], [[integrator]]s, and [[Analog delay line|delay line]]s. Nonlinear circuits include [[compandor]]s, multipliers ([[frequency mixer]]s, [[voltage-controlled amplifier]]s), [[voltage-controlled filter]]s, [[voltage-controlled oscillator]]s, and [[phase-locked loop]]s.

===Continuous Timetime===

[[Continuous signal|Continuous-time signal]] processing is for signals that vary with the change of continuous ___domain (without considering some individual interrupted points).

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In some contexts, <math>h(t)</math> is referred to as the impulse response of the system. The above [[convolution]] operation is conducted between the input and the system.

===Discrete Timetime===

[[Discrete-time signal]] processing is for sampled signals, defined only at discrete points in time, and as such are quantized in time, but not in magnitude.

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Polynomial signal processing is a type of non-linear signal processing, where [[polynomial]] systems may be interpreted as conceptually straightforward extensions of linear systems to the nonlinear case.<ref>{{cite book |author1=V. John Mathews |author2=Giovanni L. Sicuranza |title=Polynomial Signal Processing |date=May 2000 |isbn=978-0-471-03414-8 |publisher=Wiley}}</ref>

===Statistical Signal Processing ===

'''Statistical signal processing''' is an approach which treats signals as [[stochastic process]]es, utilizing their [[statistical]] properties to perform signal processing tasks .<ref name ="Scharf">{{cite book |first=Louis L. |last=Scharf |title=Statistical signal processing: detection, estimation, and time series analysis |publisher=[[Addison–Wesley]] |___location=[[Boston]] |year=1991 |isbn=0-201-19038-9 |oclc=61160161}}</ref>. Statistical techniques are widely used in signal processing applications. For example, one can model the [[probability distribution]] of noise incurred when photographing an image, and construct techniques based on this model to [[noise reduction|reduce the noise]] in the resulting image.

===Graph Signal Processing ===

'''Graph signal processing''' generalizes signal processing tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph .<ref name ="Ortega">{{cite book |first=A. |last=Ortega |title=Introduction to Graph Signal Processing |publisher=[[Cambridge University Press]] |___location=[[Cambridge]] |year=2022 |isbn=9781108552349}}</ref>. Graph signal processing presents several key points such as sampling signal techniques ,<ref name="Tanaka">{{cite journal|title=Generalized Sampling on Graphs with Subspace and Smoothness Prior|journal=IEEE Transactions on Signal Processing|date=2020|url=https://ieeexplore.ieee.org/document/9043719|last1=Tanaka|first1=Y.|last2=Eldar|first2=Y.|volume=68 |pages=2272–2286 |doi=10.1109/TSP.2020.2982325 |arxiv=1905.04441 |bibcode=2020ITSP...68.2272T }}</ref>, recovery techniques <ref name="Fascista">{{cite journal|title=Graph Signal Reconstruction under Heterogeneous Noise via Adaptive Uncertainty-Aware Sampling and Soft Classification|journal=IEEE Transactions on Signal and Information Processing over Networks|date=2024|url=https://ieeexplore.ieee.org/document/10465260|last1=Fascista|first1=A.|last2=Coluccia|first2=A.|last3=Ravazzi|first3=C.|volume=10 |pages=277–293 |doi=10.1109/TSIPN.2024.3375593 |bibcode=2024ITSIP..10..277F }}</ref> and time-varying techiques .<ref name="Giraldo">{{cite journal|title=Reconstruction of Time-varying Graph Signals via Sobolev Smoothness|journal=IEEE Transactions on Signal and Information Processing Overover Networks|date=March 2022|url=https://ieeexplore.ieee.org/document/9730033|last1=Giraldo|first1=J.|last2=Mahmood|first2=A. |last3=Garcia-Garcia|first3=B.|last4=Thanou|first4=D.|last5=Bouwmans|first5=T.|volume=8 |pages=201–214 |doi=10.1109/TSIPN.2022.3156886 |arxiv=2207.06439 |bibcode=2022ITSIP...8..201G }}</ref>. Graph signal processing has been applied with success in the field of image processing, computer vision <ref name="Giraldo1">{{cite journalbook|title=Semi-Supervised Background Subtraction of Unseen Videos: Minimization of The Total Variation of Graph Signals|journal=2020 IEEE International Conference on Image Processing, (ICIP 2020)|date=October 2020|url=https://ieeexplore.ieee.org/document/9190887|last1=Giraldo|first1=J.|last2=JavedBouwmans|first2=ST.|last2chapter=Bouwmans Semi-Supervised Background Subtraction of Unseen Videos: Minimization of the Total Variation of Graph Signals|first2pages=T 3224–3228|doi= 10.1109/ICIP40778.2020.9190887|isbn= 978-1-7281-6395-6}}</ref>

<ref name="Giraldo2">{{cite journalbook|title=GraphBGS:2020 Background25th Subtraction via Recovery of Graph Signals|journal=IInternationalInternational Conference on Pattern Recognition, (ICPR 2020)|date=2020|url=https://ieeexplore.ieee.org/document/9412999|last1=Giraldo|first1=J.|last2=Bouwmans|first2=T.|chapter=GraphBGS: Background Subtraction via Recovery of Graph Signals |pages=6881–6888 |doi=10.1109/ICPR48806.2021.9412999 |arxiv=2001.06404 |isbn=978-1-7281-8808-9 }}</ref>

<ref name="Giraldo3">{{cite journalbook|title=The Emerging Field of Graph Signal Processing for Moving Object Segmentation|journal=International Workshop on Frontiers of Computer Vision, IW-FCV 2021|date=February 2021|chapter-url=https://link.springer.com/chapter/10.1007/978-3-030-81638-4_3|last1=Giraldo|first1=J.|last2=Javed|first2=S.|last3=Sultana|first3=M.|last4=Jung|first4=S.|last5=Bouwmans|first5=T.|chapter=The Emerging Field of Graph Signal Processing for Moving Object Segmentation |series=Communications in Computer and Information Science |volume=1405 |pages=31–45 |doi=10.1007/978-3-030-81638-4_3 |isbn=978-3-030-81637-7 }}</ref> and sound anomaly detection .<ref name="Bouwmans1">{{cite journal|title=Anomalous Sound Detection for Road Surveillance based on Graph Signal Processing|journal=European Conference on Signal Processing, EUSIPCO 2024|date=2024|url=https://ieeexplore.ieee.org/document/10715291|last1=Mnasri|first1=Z.|last2=Giraldo|first2=H. |last3=Bouwmans|first3=T.|pages=161–165 |doi=10.23919/EUSIPCO63174.2024.10715291 |isbn=978-9-4645-9361-7 }}</ref>.

==Application fields==

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|title=Optimization of data-driven filterbank for automatic speaker verification

|journal=Digital Signal Processing |date=September 2020 |volume=104

|pagearticle-number=102795 |doi= 10.1016/j.dsp.2020.102795|arxiv=2007.10729|bibcode=2020DSP...10402795S |s2cid=220665533 }}</ref>

* [[Image processing]]{{spaced ndash}} in digital cameras, computers and various imaging systems

* [[Video processing]]{{spaced ndash}} for interpreting moving pictures

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* [[Filter (signal processing)|Filters]]{{spaced ndash}} for example analog (passive or active) or digital ([[FIR filter|FIR]], [[IIR filter|IIR]], frequency ___domain or [[stochastic filter]]s, etc.)

* [[Sampling (signal processing)|Samplers]] and [[analog-to-digital converter]]s for [[signal acquisition]] and reconstruction, which involves measuring a physical signal, storing or transferring it as digital signal, and possibly later rebuilding the original signal or an approximation thereof.

* [[Digital signal processor]]s (DSPs)<!--[[User:Kvng/RTH]]-->

==Mathematical methods applied==

* [[Differential equations]]<ref name="Gaydecki2004">{{cite book|author=Patrick Gaydecki|title=Foundations of Digital Signal Processing: Theory, Algorithms and Hardware Design|url=https://books.google.com/books?id=6Qo7NvX3vz4C&q=%22differential+equation%22+OR+%22differential+equations%22&pg=PA40|year=2004|publisher=IET|isbn=978-0-85296-431-6|pages=40–}}</ref>{{spaced -ndash}} for modeling system behavior, connecting input and output relations in linear time-invariant systems. For instance, a low-pass filter such as an [[RC circuit]] can be modeled as a differential equation in signal processing, which allows one to compute the continuous output signal as a function of the input or initial conditions.

* [[Recurrence relation]]s<ref name="Engelberg2008">{{cite book|author=Shlomo Engelberg|title=Digital Signal Processing: An Experimental Approach|url=https://books.google.com/books?id=z3CpcCHbtgIC|date=8 January 2008|publisher=Springer Science & Business Media|isbn=978-1-84800-119-0}}</ref>

* [[Transform theory]]

* [[Time-frequency analysis]]{{spaced ndash}} for processing non-stationary signals<ref>{{cite book|title=Time frequency signal analysis and processing a comprehensive reference|year=2003|publisher=Elsevier|___location=Amsterdam|isbn=0-08-044335-4|edition=1|editor=Boashash, Boualem}}</ref>

* [[Linear canonical transformation]]

* [[Spectral estimation]]{{spaced ndash}} for determining the spectral content (i.e., the distribution of power over frequency) of a set of [[time series]] data points<ref>{{cite book|first1=Petre|last1=Stoica|first2=Randolph|last2=Moses|title=Spectral Analysis of Signals|year=2005|publisher=Prentice Hall|___location=NJ|url=http://user.it.uu.se/%7Eps/SAS-new.pdf}}</ref>

*[[Statistical signal processing]]{{spaced ndash}} analyzing and extracting information from signals and noise based on their stochastic properties

*[[Linear time-invariant system]] theory, and [[transform theory]]

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*[[System identification]]<ref name="Billings"/> and classification

*[[Calculus]]

*[[CodeCoding theory]]

*[[Complex analysis]]<ref name="SchreierScharf2010">{{cite book|author1=Peter J. Schreier|author2=Louis L. Scharf|title=Statistical Signal Processing of Complex-Valued Data: The Theory of Improper and Noncircular Signals|url=https://books.google.com/books?id=HBaxLfDsAHoC&q=%22complex+analysis%22|date=4 February 2010|publisher=Cambridge University Press|isbn=978-1-139-48762-7}}</ref>

*[[Vector spaces]] and [[Linear algebra]]<ref name="Little2019">{{cite book|author=Max A. Little|title=Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics|url=https://books.google.com/books?id=ejGoDwAAQBAJ&q=%22vector+space%22|date=13 August 2019|publisher=OUP Oxford|isbn=978-0-19-102431-3}}</ref>

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*[[Optimization]]<ref name="PalomarEldar2010">{{cite book|author1=Daniel P. Palomar|author2=Yonina C. Eldar|title=Convex Optimization in Signal Processing and Communications|url=https://books.google.com/books?id=UOpnvPJ151gC|year=2010|publisher=Cambridge University Press|isbn=978-0-521-76222-9}}</ref>

*[[Numerical methods]]

*[[Time series]]

*[[Data mining]]{{spaced ndash}} for statistical analysis of relations between large quantities of variables (in this context representing many physical signals), to extract previously unknown interesting patterns

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* [[Audio filter]]

* [[Bounded variation]]

* [[DigitalDynamic imagerange processingcompression]]

* [[Dynamic range compression]], [[companding]], [[limiting]], and [[noise gating]]

* [[Fourier transform]]

* [[Information theory]]

* [[Least-squares spectral analysis]]

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==Further reading==

* {{cite book |first=Charles |last=Byrne |title=Signal Processing: A Mathematical Approach |publisher=[[Taylor & Francis]] |year=2014 |doi=10.1201/b17672 |isbn=9780429158711 |url=https://www.taylorfrancis.com/books/oa-mono/10.1201/b17672/signal-processing-charles-byrne}}

* {{cite book|last=P Stoica|first=R Moses|title=Spectral Analysis of Signals|year=2005|publisher=Prentice Hall|___location=NJ|url=http://user.it.uu.se/%7Eps/SAS-new.pdf}}

* {{cite book |firstlast=Steven M.P Stoica|lastfirst=KayR Moses|title=FundamentalsSpectral Analysis of Statistical Signal Processing Signals|year=2005|publisher=[[Prentice Hall]] |___location=[[Upper Saddle River, New Jersey]] NJ|yearurl=1993 |isbn=0https://user.it.uu.se/%7Eps/SAS-13-345711-7 |oclc=26504848new.pdf}}

* {{cite book |first=Athanasios |last=Papoulis |title=Probability, Random Variables, and Stochastic Processes |year=1991 |edition=third |publisher=McGraw-Hill |isbn=0-07-100870-5}}

* Kainam Thomas Wong [http://www.eie.polyu.edu.hk/~enktwong/]: Statistical Signal Processing lecture notes at the University of Waterloo, Canada.

* [[Ali H. Sayed]], Adaptive Filters, Wiley, NJ, 2008, {{isbn|978-0-470-25388-5}}.

* [[Thomas Kailath]], [[Ali H. Sayed]], and [[Babak Hassibi]], Linear Estimation, Prentice-Hall, NJ, 2000, {{isbn|978-0-13-022464-4}}.<!--[[User:Kvng/RTH]]-->

==External links==

* [https://www.sp4comm.org/ Signal Processing for Communications] – free online textbook by Paolo Prandoni and Martin Vetterli (2008)

* [httphttps://www.dspguide.com Scientists and Engineers Guide to Digital Signal Processing] – free online textbook by Stephen Smith

* [https://www.dsprelated.com/freebooks/sasp/ Julius O. Smith III: Spectral Audio Signal Processing] – free online textbook

* [https://sites.google.com/view/gsp-website/graph-signal-processing Graph Signal Processing Website] – free online website by Thierry Bouwmans (2025)

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