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{{short description|Mathematical signal manipulation by computers}}
[[de:DSP]]
{{Redirect|Digital transform|the impact of digital technology on society|Digital transformation}}
'''Digital signal processing''' (DSP) is the study of [[signal]]s in a [[digital]] representation and the processing methods of these signals. DSP is, together with [[analog signal processing]] a subset of [[signal processing]].


{{More citations needed|date=May 2008}}
In DSP, engineers most commonly study digital signals in one of the following domains: [[time domain]] (one dimensional signals), spatial domain (multidimensional signals), [[frequency]] domain, [[autocorrelation]] domain, and [[wavelet]] domains.

They choose the domain in which to process a signal by making an educated guess (or trying out different possibilities) as to which domain best represents the essential characteristics of the signal.
'''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>
A sequence of samples from a measuring device produces a time or spatial domain representation,

whereas a [[discrete Fourier transform]] produces the frequency domain information.
Digital signal processing and [[analog signal processing]] are subfields of signal processing. DSP applications include [[Audio signal processing|audio]] and [[speech processing]], [[sonar]], [[radar]] and other [[sensor array]] processing, [[spectral density estimation]], [[statistical signal processing]], [[digital image processing]], [[data compression]], [[video coding]], [[audio coding]], [[image compression]], signal processing for [[telecommunications]], [[control system]]s, [[biomedical engineering]], and [[seismology]], among others.
The autocorrelation is, loosely speaking, defined as the expected value of correlation of the signal with itself on some distance in time or spatial distance.

DSP can involve linear or nonlinear operations. Nonlinear signal processing is closely related to [[nonlinear system identification]]<ref>{{cite book |last=Billings |first=Stephen A. |title=Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains |publisher=Wiley |isbn=978-1-119-94359-4 |date=Sep 2013 |location=UK}}</ref> and can be implemented in the [[Time domain|time]], [[Frequency domain|frequency]], and [[Spacetime|spatio-temporal domains]].<!--sort of a flip stab at a wikilink for this concept. Readers ''might'' get the idea.-->

The application of digital computation to signal processing allows for many advantages over analog processing in many applications, such as [[error detection and correction]] in transmission as well as [[data compression]].<ref>{{cite book |title=Digital Signal Processing: Instant access |last1=Broesch |first1=James D. |last2=Stranneby |first2=Dag |last3=Walker |first3=William |date=2008-10-20 |publisher=Butterworth-Heinemann-Newnes |edition=1 |isbn=9780750689762 |page=3}}</ref> Digital signal processing is also fundamental to [[digital electronics|digital technology]], such as [[digital telecommunication]] and [[wireless communications]].<ref name="Srivastava">{{cite book |last1=Srivastava |first1=Viranjay M. |last2=Singh |first2=Ghanshyam |title=MOSFET Technologies for Double-Pole Four-Throw Radio-Frequency Switch |date=2013 |publisher=[[Springer Science & Business Media]] |isbn=9783319011653 |page=1 |url=https://books.google.com/books?id=fkO9BAAAQBAJ&pg=PA1}}</ref> DSP is applicable to both [[streaming data]] and static (stored) data.


== Signal sampling ==
== Signal sampling ==
{{Main|Sampling (signal processing)}}


To digitally analyze and manipulate an analog signal, it must be digitized with an [[analog-to-digital converter]] (ADC).<ref>{{cite journal |last=Walden |first=R. H. |date=1999 |title=Analog-to-digital converter survey and analysis |journal=IEEE Journal on Selected Areas in Communications |volume=17 |issue=4 |pages=539–550 |doi=10.1109/49.761034}}</ref> Sampling is usually carried out in two stages, [[discretization]] and [[Quantization (signal processing)|quantization]]. Discretization means that the signal is divided into equal intervals of time, and each interval is represented by a single measurement of amplitude. Quantization means each amplitude measurement is approximated by a value from a finite set. Rounding [[real numbers]] to integers is an example.
A digital signal is often a numerical representation of a continuous signal. This discrete representation of a continuous signal will generally introduce some error in to the data. The accuracy of the representation is mostly dependent on two things; [[sampling frequency]] and the number of bits used for the representation. The continuous signal is usually sampled at regular intervals and the value of the continuous signal in that interval is represented by a discrete value. The sampling frequency or sampling rate is then the rate at which new samples are taken from the continuous signal. The number of bits used for one value of the discrete signal tells us how accurately the signal magnitude is represented. Similarly, the sampling frequency controls the temporal or spatial accuracy of the discrete signal.


The [[Nyquist–Shannon sampling theorem]] states that a signal can be exactly reconstructed from its samples if the sampling frequency is greater than twice the highest frequency component in the signal. In practice, the sampling frequency is often significantly higher than this.<ref>{{cite journal |last1=Candes |first1=E. J. |last2=Wakin |first2=M. B. |date=2008 |title=An Introduction To Compressive Sampling |journal=IEEE Signal Processing Magazine |volume=25 |issue=2 |pages=21–30 |doi=10.1109/MSP.2007.914731|bibcode=2008ISPM...25...21C |s2cid=1704522 |url=https://resolver.caltech.edu/CaltechAUTHORS:CANieeespm08 }}</ref> It is common to use an [[anti-aliasing filter]] to limit the signal bandwidth to comply with the sampling theorem, however careful selection of this filter is required because the reconstructed signal will be the filtered signal plus residual [[aliasing]] from imperfect [[stop band]] rejection instead of the original (unfiltered) signal.
The [[Nyquist-Shannon sampling theorem]], a fundamental theorem of signal processing, states that a sampled signal cannot unambiguously represent signal components with frequencies above half the sampling frequency. This frequency (half the sampling frequency) is called the [[Nyquist frequency]]. Frequencies above the Nyquist frequency N can be observed in the digital signal, but their frequency is ambiguous. That is, a frequency component with frequency f cannot be distinguished from another component with frequency 2N-f, 2N+f, 4N-f, etc. This is called [[aliasing]]. To handle this problem as gracefully as possible, most analog signals are filtered with an [[anti-aliasing]] filter (usually a [[low-pass filter]]) at the Nyquist frequency before conversion to the digital representation.


Theoretical DSP analyses and derivations are typically performed on [[discrete-time signal]] models with no amplitude inaccuracies ([[quantization error]]), created by the abstract process of [[Sampling (signal processing)|sampling]]. Numerical methods require a quantized signal, such as those produced by an ADC. The processed result might be a frequency spectrum or a set of statistics. But often it is another quantized signal that is converted back to analog form by a [[digital-to-analog converter]] (DAC).
== Time and spatial domains ==


== Domains ==
The most common processing approach in the time or spatial domain is enhancement of the input signal through a method called filtering.
DSP engineers usually study digital signals in one of the following domains: [[time domain]] (one-dimensional signals), spatial domain (multidimensional signals), [[frequency domain]], and [[wavelet]] domains. They choose the domain in which to process a signal by making an informed assumption (or by trying different possibilities) as to which domain best represents the essential characteristics of the signal and the processing to be applied to it. A sequence of samples from a measuring device produces a temporal or spatial domain representation, whereas a [[discrete Fourier transform]] produces the frequency domain representation.
Filtering consists generally of some transformation of a number of surrounding samples around the current sample of the input and/or output signal.
Properties such as the following characterize filters:
* A "linear" filter consists of a [[linear transformation]] of input samples; other filters are "non-linear."
* A "causal" transformation uses only previous samples of the input or output signals; transformations that also use future input samples are "non-causal." Adding a delay will transform many non-causal filters into causal filters.
* A "time-invariant" filter has constant properties over time; other filters such as adaptive filters change in time.
* "Finite impulse response" (FIR) filters use only the input signal; so-called "infinite impulse response" filters use both the input signal and previous samples of the output signal.


=== Time and space domains ===
Most filters can, in Z-domain (frequency domain is a subset of Z-domain), be described by their [[Transfer function]]s.
[[Time domain]] refers to the analysis of signals with respect to time. Similarly, space domain refers to the analysis of signals with respect to position, e.g., pixel location for the case of image processing.


The most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. [[Digital filter]]ing generally consists of some linear transformation of a number of surrounding samples around the current sample of the input or output signal. The surrounding samples may be identified with respect to time or space. The output of a linear digital filter to any given input may be calculated by [[convolution|convolving]] the input signal with an [[impulse response]].
== Frequency domain ==


=== Frequency domain ===
Signals are converted from time or spatial domain to the frequency domain usually through the Fourier transform. In Fourier transform the signal information is converted to a magnitude and phase component of each frequency. Regurarly, the Fourier transform is converted to the power spectrum, which is the magnitude of each frequency component squared.
{{Main|Frequency domain}}


The most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum to get information of which frequencies are present in the input signal and which are missing. However, there are some commonly used frequency domain transformations, for example, the cepstrum. In generation of the cepstrum, a signal is converted to the frequency domain through Fourier transform, then the logarithm is of the spectrum, which is converted back to time domain through the inverse Fourier transform. In the cepstrum, frequency components with smaller magnitude are thus emphasised while retaining the order of magnitudes of frequency components.
Signals are converted from time or space domain to the frequency domain usually through use of the [[Fourier transform]]. The Fourier transform converts the time or space information to a magnitude and phase component of each frequency. With some applications, how the phase varies with frequency can be a significant consideration. Where phase is unimportant, often the Fourier transform is converted to the power spectrum, which is the magnitude of each frequency component squared.


The most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum to determine which frequencies are present in the input signal and which are missing. Frequency domain analysis is also called ''spectrum-'' or ''spectral analysis''.
== Applications ==


Filtering, particularly in non-realtime work can also be achieved in the frequency domain, applying the filter and then converting back to the time domain. This can be an efficient implementation and can give essentially any filter response including excellent approximations to [[brickwall filter]]s.
Typical applications of digital signal processing are, for example, speech compression and transmission in (digital) [[mobile phone]]s, equalisation of sound in Hifi-equipment, [[weather forecasting]] and economic forecasting, analysis and control of industrial processes, computer-generated animations in movies and image manipulation.


There are some commonly used frequency domain transformations. For example, the [[cepstrum]] converts a signal to the frequency domain through Fourier transform, takes the logarithm, then applies another Fourier transform. This emphasizes the harmonic structure of the original spectrum.
Major subfields:
* [[Audio signal processing]]
* [[Digital image processing]]
* [[Speech processing]]


===Z-plane analysis===
Techniques:
Digital filters come in both [[infinite impulse response]] (IIR) and [[finite impulse response]] (FIR) types. Whereas FIR filters are always stable, IIR filters have feedback loops that may become unstable and oscillate. The [[Z-transform]] provides a tool for analyzing stability issues of digital IIR filters. It is analogous to the [[Laplace transform]], which is used to design and analyze analog IIR filters.
* [[Filter design]]

** [[Transfer function]]
===Autoregression analysis===
A signal is represented as linear combination of its previous samples. Coefficients of the combination are called autoregression coefficients. This method has higher frequency resolution and can process shorter signals compared to the Fourier transform.<ref name = "Marple">{{Cite book| publisher = Prentice Hall| isbn = 978-0-13-214149-9| last = Marple| first = S. Lawrence| title = Digital Spectral Analysis: With Applications| location = Englewood Cliffs, N.J| date = 1987-01-01}}</ref> [[Prony's method]] can be used to estimate phases, amplitudes, initial phases and decays of the components of signal.<ref name = "Ribeiro" /><ref name = "Marple" /> Components are assumed to be complex decaying exponents.<ref name = "Ribeiro">{{Cite journal| doi = 10.1006/mssp.2001.1399| issn = 0888-3270| volume = 17| issue = 3| pages = 533–549| last1 = Ribeiro| first1 = M.P.| last2 = Ewins| first2 = D.J.| last3 = Robb| first3 = D.A.| title = Non-stationary analysis and noise filtering using a technique extended from the original Prony method| journal = Mechanical Systems and Signal Processing| access-date = 2019-02-17| date = 2003-05-01| bibcode = 2003MSSP...17..533R| url = http://linkinghub.elsevier.com/retrieve/pii/S0888327001913998}}</ref><ref name = "Marple" />

===Time-frequency analysis===
A time-frequency representation of signal can capture both temporal evolution and frequency structure of analyzed signal. Temporal and frequency resolution are limited by the principle of uncertainty and the tradeoff is adjusted by the width of analysis window. Linear techniques such as [[Short-time Fourier transform]], [[wavelet transform]], [[filter bank]],<ref>{{Cite conference| last1 = So| first1 = Stephen| last2 = Paliwal| first2 = Kuldip K.| title = Improved noise-robustness in distributed speech recognition via perceptually-weighted vector quantisation of filterbank energies| book-title = Ninth European Conference on Speech Communication and Technology| date = 2005}}</ref> non-linear (e.g., [[Wigner–Ville transform]]<ref name = "Ribeiro" />) and [[autoregressive]] methods (e.g. segmented Prony method)<ref name = "Ribeiro" /><ref>{{Cite journal| doi = 10.1515/acgeo-2015-0012| issn = 1895-6572| volume = 63| issue = 3| pages = 652–678| last1 = Mitrofanov| first1 = Georgy| last2 = Priimenko| first2 = Viatcheslav| title = Prony Filtering of Seismic Data| journal = Acta Geophysica| date = 2015-06-01| bibcode = 2015AcGeo..63..652M| s2cid = 130300729| doi-access = free}}</ref><ref>{{Cite journal| doi = 10.20403/2078-0575-2020-2-55-67| issn = 2078-0575| issue = 2| pages = 55–67| last1 = Mitrofanov| first1 = Georgy| last2 = Smolin| first2 = S. N.| last3 = Orlov| first3 = Yu. A.| last4 = Bespechnyy| first4 = V. N.| title = Prony decomposition and filtering| journal = Geology and Mineral Resources of Siberia| access-date = 2020-09-08| date = 2020| s2cid = 226638723| url = http://www.jourgimss.ru/en/SitePages/catalog/2020/02/abstract/2020_2_55.aspx}}</ref> are used for representation of signal on the time-frequency plane. Non-linear and segmented Prony methods can provide higher resolution, but may produce undesirable artifacts. Time-frequency analysis is usually used for analysis of non-stationary signals. For example, methods of [[fundamental frequency]] estimation, such as RAPT and PEFAC<ref>{{Cite journal| doi = 10.1109/TASLP.2013.2295918| issn = 2329-9290| volume = 22| issue = 2| pages = 518–530| last1 = Gonzalez| first1 = Sira| last2 = Brookes| first2 = Mike| title = PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise| journal = IEEE/ACM Transactions on Audio, Speech, and Language Processing| access-date = 2017-12-03| date = February 2014| s2cid = 13161793| url = https://ieeexplore.ieee.org/document/6701334}}</ref> are based on windowed spectral analysis.

===Wavelet===
[[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===
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>

== 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>

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]].

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>

'''{{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.

== Applications ==
General application areas for DSP include
{{Div col|colwidth=20em}}
*[[Audio signal processing]]
*[[Audio data compression]] e.g. [[MP3]]
*[[Video data compression]]
*[[Computer graphics]]
*[[Digital image processing]]
*[[Photo manipulation]]
*[[Speech processing]]
*[[Speech recognition]]
*[[Data transmission]]
*[[Radar]]
*[[Sonar]]
*[[Financial signal processing]]
*[[Economic forecasting]]
*[[Seismology]]
*[[Biomedicine]]
*[[Weather forecasting]]
{{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>

== Techniques ==
{{Div col|colwidth=20em}}
* [[Bilinear transform]]
* [[Bilinear transform]]
* [[Discrete Fourier transform]]
* [[Discrete-time Fourier transform]]
* [[Filter design]]
* [[Goertzel algorithm]]
* [[Least-squares spectral analysis]]
* [[LTI system theory]]
* [[Minimum phase]]
* [[s-plane]]
* [[Transfer function]]
* [[Z-transform]]
{{Div col end}}


Related fields:
== Related fields ==
{{Div col|colwidth=20em}}
* [[Acoustics]]
* [[Computer Science]]
* [[Analog signal processing]]
* [[Automatic control]]
* [[Computer engineering]]
* [[Computer science]]
* [[Data compression]]
* [[Data compression]]
* [[Dataflow programming]]
* [[Discrete cosine transform]]
* [[Electrical engineering]]
* [[Electrical engineering]]
* [[Fourier analysis]]
* [[Information theory]]
* [[Information theory]]
* [[Telecommunication]]
* [[Machine learning]]
* [[Real-time computing]]
* [[Stream processing]]
* [[Telecommunications]]
* [[Time series]]
* [[Wavelet]]
{{Div col end}}
== Further reading ==
{{wikibooks|Digital Signal Processing}}
{{refbegin|30em}}
*{{cite book | last1 = Ahmed | first1 = Nasir | author-link1 = Nasir Ahmed (engineer) | last2 = Rao | first2 = Kamisetty Ramamohan | title = ICASSP '76. IEEE International Conference on Acoustics, Speech, and Signal Processing | chapter = Orthogonal transforms for digital signal processing | author-link2 = K. R. Rao | date = 1975-08-07 | volume = 1 | pages = 136–140 | publisher = [[Springer Science+Business Media|Springer-Verlag]] | publication-place = New York | doi = 10.1109/ICASSP.1976.1170121 | isbn = 978-3540065562 | lccn = 73018912 | ol = OL22806004M | oclc = 438821458 | s2cid = 10776771 | df = dmy-all}}
*Jonathan M. Blackledge, Martin Turner: ''Digital Signal Processing: Mathematical and Computational Methods, Software Development and Applications'', Horwood Publishing, {{ISBN|1-898563-48-9}}
*James D. Broesch: ''Digital Signal Processing Demystified'', Newnes, {{ISBN|1-878707-16-7}}
*{{ cite book | editor-last1 = Yovits | editor-first1 = Marshall C. | last1 = Dyer | first1 = Stephen A. | last2 = Harms | first2 = Brian K. | chapter = Digital Signal Processing | title = Advances in Computers | date = 1993-08-13 | volume = 37 | pages = 59{{hyphen}}118 | publisher = [[Academic Press]] | doi = 10.1016/S0065-2458(08)60403-9 | isbn = 978-0120121373 | issn = 0065-2458 | lccn = 59015761 | chapter-url = https://books.google.com/books?id=vL-bB7GALAwC&pg=PA104 | ol = OL10070096M | oclc = 858439915 | df = dmy-all}}
*Paul M. Embree, Damon Danieli: ''C++ Algorithms for Digital Signal Processing'', Prentice Hall, {{ISBN|0-13-179144-3}}
*Hari Krishna Garg: ''Digital Signal Processing Algorithms'', CRC Press, {{ISBN|0-8493-7178-3}}
*P. Gaydecki: ''Foundations Of Digital Signal Processing: Theory, Algorithms And Hardware Design'', Institution of Electrical Engineers, {{ISBN|0-85296-431-5}}
*Ashfaq Khan: ''Digital Signal Processing Fundamentals'', Charles River Media, {{ISBN|1-58450-281-9}}
*Sen M. Kuo, Woon-Seng Gan: ''Digital Signal Processors: Architectures, Implementations, and Applications'', Prentice Hall, {{ISBN|0-13-035214-4}}
*Paul A. Lynn, Wolfgang Fuerst: ''Introductory Digital Signal Processing with Computer Applications'', John Wiley & Sons, {{ISBN|0-471-97984-8}}
*Richard G. Lyons: ''Understanding Digital Signal Processing'', Prentice Hall, {{ISBN|0-13-108989-7}}
*Vijay Madisetti, Douglas B. Williams: ''The Digital Signal Processing Handbook'', CRC Press, {{ISBN|0-8493-8572-5}}
*[[James H. McClellan]], [[Ronald W. Schafer]], Mark A. Yoder: ''Signal Processing First'', Prentice Hall, {{ISBN|0-13-090999-8}}
*Bernard Mulgrew, Peter Grant, John Thompson: ''Digital Signal Processing – Concepts and Applications'', Palgrave Macmillan, {{ISBN|0-333-96356-3}}
*Boaz Porat: ''A Course in Digital Signal Processing'', Wiley, {{ISBN|0-471-14961-6}}
*John G. Proakis, [[Dimitris Manolakis]]: ''Digital Signal Processing: Principles, Algorithms and Applications'', 4th ed, Pearson, April 2006, {{ISBN|978-0131873742}}
*John G. Proakis: ''A Self-Study Guide for Digital Signal Processing'', Prentice Hall, {{ISBN|0-13-143239-7}}
*Charles A. Schuler: ''Digital Signal Processing: A Hands-On Approach'', McGraw-Hill, {{ISBN|0-07-829744-3}}
*Doug Smith: ''Digital Signal Processing Technology: Essentials of the Communications Revolution'', American Radio Relay League, {{ISBN|0-87259-819-5}}
*{{cite book|url=http://www.dspguide.com|title=Digital Signal Processing: A Practical Guide for Engineers and Scientists|last=Smith|first=Steven W.|date=2002|publisher=Newnes|isbn=0-7506-7444-X}}
*{{cite book|title =Digital Signal Processing, a Computer Science Perspective|last =Stein|first =Jonathan Yaakov|date =2000-10-09|publisher =Wiley|isbn =0-471-29546-9}}
*{{cite book|title =Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real-Time Systems|last =Stergiopoulos|first =Stergios|date =2000|publisher =CRC Press|isbn =0-8493-3691-0}}
*{{cite book|title =Fundamentals of Digital Signal Processing|last =Van De Vegte|first =Joyce|date =2001|publisher =Prentice Hall|isbn =0-13-016077-6}}
*{{Cite book|title =Discrete-Time Signal Processing|last1=Oppenheim|first1=Alan V.|last2=Schafer|first2=Ronald W.|publisher=Pearson|year=2001|isbn=1-292-02572-7}}
*Hayes, Monson H. Statistical digital signal processing and modeling. John Wiley & Sons, 2009. (with [https://www.mathworks.com/matlabcentral/fileexchange/2183-statistical-digital-signal-processing-and-modeling?s_tid=prof_contriblnk MATLAB scripts])
{{refend}}

== References==
{{Reflist}}

{{Digital systems}}
{{DSP}}


{{Authority control}}
==External Link==


*The Scientist and Engineer's Guide to Digital Signal Processing (http://www.dspguide.com )
{{DEFAULTSORT:Digital Signal Processing}}
[[Category:Digital signal processing| ]]
[[Category:Digital electronics]]
[[Category:Computer engineering]]
[[Category:Telecommunication theory]]
[[Category:Radar signal processing]]

Latest revision as of 13:46, 8 December 2024

"Digital transform" redirects here. For the impact of digital technology on society, see Digital transformation.

Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations. The digital signals processed in this manner are a sequence of numbers that represent 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,[1][2] which is typically generated by the switching of a transistor.[3]

Digital signal processing and analog signal processing are subfields of signal processing. DSP applications include audio and speech processing, sonar, radar and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, data compression, video coding, audio coding, image compression, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others.

DSP can involve linear or nonlinear operations. Nonlinear signal processing is closely related to nonlinear system identification[4] and can be implemented in the time, frequency, and spatio-temporal domains.

The application of digital computation to signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression.[5] Digital signal processing is also fundamental to digital technology, such as digital telecommunication and wireless communications.[6] DSP is applicable to both streaming data and static (stored) data.

Signal sampling

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Main article: Sampling (signal processing)

To digitally analyze and manipulate an analog signal, it must be digitized with an analog-to-digital converter (ADC).[7] Sampling is usually carried out in two stages, discretization and quantization. Discretization means that the signal is divided into equal intervals of time, and each interval is represented by a single measurement of amplitude. Quantization means each amplitude measurement is approximated by a value from a finite set. Rounding real numbers to integers is an example.

The Nyquist–Shannon sampling theorem states that a signal can be exactly reconstructed from its samples if the sampling frequency is greater than twice the highest frequency component in the signal. In practice, the sampling frequency is often significantly higher than this.[8] It is common to use an anti-aliasing filter to limit the signal bandwidth to comply with the sampling theorem, however careful selection of this filter is required because the reconstructed signal will be the filtered signal plus residual aliasing from imperfect stop band rejection instead of the original (unfiltered) signal.

Theoretical DSP analyses and derivations are typically performed on discrete-time signal models with no amplitude inaccuracies (quantization error), created by the abstract process of sampling. Numerical methods require a quantized signal, such as those produced by an ADC. The processed result might be a frequency spectrum or a set of statistics. But often it is another quantized signal that is converted back to analog form by a digital-to-analog converter (DAC).

Domains

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DSP engineers usually study digital signals in one of the following domains: time domain (one-dimensional signals), spatial domain (multidimensional signals), frequency domain, and wavelet domains. They choose the domain in which to process a signal by making an informed assumption (or by trying different possibilities) as to which domain best represents the essential characteristics of the signal and the processing to be applied to it. A sequence of samples from a measuring device produces a temporal or spatial domain representation, whereas a discrete Fourier transform produces the frequency domain representation.

Time and space domains

[edit]

Time domain refers to the analysis of signals with respect to time. Similarly, space domain refers to the analysis of signals with respect to position, e.g., pixel location for the case of image processing.

The most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Digital filtering generally consists of some linear transformation of a number of surrounding samples around the current sample of the input or output signal. The surrounding samples may be identified with respect to time or space. The output of a linear digital filter to any given input may be calculated by convolving the input signal with an impulse response.

Frequency domain

[edit]
Main article: Frequency domain

Signals are converted from time or space domain to the frequency domain usually through use of the Fourier transform. The Fourier transform converts the time or space information to a magnitude and phase component of each frequency. With some applications, how the phase varies with frequency can be a significant consideration. Where phase is unimportant, often the Fourier transform is converted to the power spectrum, which is the magnitude of each frequency component squared.

The most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum to determine which frequencies are present in the input signal and which are missing. Frequency domain analysis is also called spectrum- or spectral analysis.

Filtering, particularly in non-realtime work can also be achieved in the frequency domain, applying the filter and then converting back to the time domain. This can be an efficient implementation and can give essentially any filter response including excellent approximations to brickwall filters.

There are some commonly used frequency domain transformations. For example, the cepstrum converts a signal to the frequency domain through Fourier transform, takes the logarithm, then applies another Fourier transform. This emphasizes the harmonic structure of the original spectrum.

Z-plane analysis

[edit]

Digital filters come in both infinite impulse response (IIR) and finite impulse response (FIR) types. Whereas FIR filters are always stable, IIR filters have feedback loops that may become unstable and oscillate. The Z-transform provides a tool for analyzing stability issues of digital IIR filters. It is analogous to the Laplace transform, which is used to design and analyze analog IIR filters.

Autoregression analysis

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A signal is represented as linear combination of its previous samples. Coefficients of the combination are called autoregression coefficients. This method has higher frequency resolution and can process shorter signals compared to the Fourier transform.[9] Prony's method can be used to estimate phases, amplitudes, initial phases and decays of the components of signal.[10][9] Components are assumed to be complex decaying exponents.[10][9]

Time-frequency analysis

[edit]

A time-frequency representation of signal can capture both temporal evolution and frequency structure of analyzed signal. Temporal and frequency resolution are limited by the principle of uncertainty and the tradeoff is adjusted by the width of analysis window. Linear techniques such as Short-time Fourier transform, wavelet transform, filter bank,[11] non-linear (e.g., Wigner–Ville transform[10]) and autoregressive methods (e.g. segmented Prony method)[10][12][13] are used for representation of signal on the time-frequency plane. Non-linear and segmented Prony methods can provide higher resolution, but may produce undesirable artifacts. Time-frequency analysis is usually used for analysis of non-stationary signals. For example, methods of fundamental frequency estimation, such as RAPT and PEFAC[14] are based on windowed spectral analysis.

Wavelet

[edit]
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 wavelets are discretely sampled. As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and location information. The accuracy of the joint time-frequency resolution is limited by the uncertainty principle of time-frequency.

Empirical mode decomposition

[edit]

Empirical mode decomposition is based on decomposition signal into intrinsic mode functions (IMFs). IMFs are quasiharmonical oscillations that are extracted from the signal.[15]

Implementation

[edit]

DSP algorithms may be run on general-purpose computers[16] and digital signal processors.[17] DSP algorithms are also implemented on purpose-built hardware such as application-specific integrated circuit (ASICs).[18] Additional technologies for digital signal processing include more powerful general purpose microprocessors, graphics processing units, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial applications such as motor control), and stream processors.[19]

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 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 photographs with software such as Photoshop.

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 FPGAs may be used.[20] For the most demanding applications or high-volume products, ASICs 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.[21]

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 workstations such as Logic Pro, Cubase, Digital Performer and Pro Tools LE use native processing. Others, such as Pro Tools HD, Universal Audio's UAD-1 and TC Electronic's Powercore use DSP processing.

Applications

[edit]

General application areas for DSP include

Specific examples include speech coding and transmission in digital mobile phones, room correction of sound in hi-fi and sound reinforcement applications, analysis and control of industrial processes, medical imaging such as CAT scans and MRI, audio crossovers and equalization, digital synthesizers, and audio effects units.[22] 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.[23]

Techniques

[edit]
[edit]

Further reading

[edit]
Wikibooks has a book on the topic of: Digital Signal Processing
  • Ahmed, Nasir; Rao, Kamisetty Ramamohan (7 August 1975). "Orthogonal transforms for digital signal processing". ICASSP '76. IEEE International Conference on Acoustics, Speech, and Signal Processing. Vol. 1. New York: Springer-Verlag. pp. 136–140. doi:10.1109/ICASSP.1976.1170121. ISBN 978-3540065562. LCCN 73018912. OCLC 438821458. OL 22806004M. S2CID 10776771.
  • Jonathan M. Blackledge, Martin Turner: Digital Signal Processing: Mathematical and Computational Methods, Software Development and Applications, Horwood Publishing, ISBN 1-898563-48-9
  • James D. Broesch: Digital Signal Processing Demystified, Newnes, ISBN 1-878707-16-7
  • Dyer, Stephen A.; Harms, Brian K. (13 August 1993). "Digital Signal Processing". In Yovits, Marshall C. (ed.). Advances in Computers. Vol. 37. Academic Press. pp. 59–118. doi:10.1016/S0065-2458(08)60403-9. ISBN 978-0120121373. ISSN 0065-2458. LCCN 59015761. OCLC 858439915. OL 10070096M.
  • Paul M. Embree, Damon Danieli: C++ Algorithms for Digital Signal Processing, Prentice Hall, ISBN 0-13-179144-3
  • Hari Krishna Garg: Digital Signal Processing Algorithms, CRC Press, ISBN 0-8493-7178-3
  • P. Gaydecki: Foundations Of Digital Signal Processing: Theory, Algorithms And Hardware Design, Institution of Electrical Engineers, ISBN 0-85296-431-5
  • Ashfaq Khan: Digital Signal Processing Fundamentals, Charles River Media, ISBN 1-58450-281-9
  • Sen M. Kuo, Woon-Seng Gan: Digital Signal Processors: Architectures, Implementations, and Applications, Prentice Hall, ISBN 0-13-035214-4
  • Paul A. Lynn, Wolfgang Fuerst: Introductory Digital Signal Processing with Computer Applications, John Wiley & Sons, ISBN 0-471-97984-8
  • Richard G. Lyons: Understanding Digital Signal Processing, Prentice Hall, ISBN 0-13-108989-7
  • Vijay Madisetti, Douglas B. Williams: The Digital Signal Processing Handbook, CRC Press, ISBN 0-8493-8572-5
  • James H. McClellan, Ronald W. Schafer, Mark A. Yoder: Signal Processing First, Prentice Hall, ISBN 0-13-090999-8
  • Bernard Mulgrew, Peter Grant, John Thompson: Digital Signal Processing – Concepts and Applications, Palgrave Macmillan, ISBN 0-333-96356-3
  • Boaz Porat: A Course in Digital Signal Processing, Wiley, ISBN 0-471-14961-6
  • John G. Proakis, Dimitris Manolakis: Digital Signal Processing: Principles, Algorithms and Applications, 4th ed, Pearson, April 2006, ISBN 978-0131873742
  • John G. Proakis: A Self-Study Guide for Digital Signal Processing, Prentice Hall, ISBN 0-13-143239-7
  • Charles A. Schuler: Digital Signal Processing: A Hands-On Approach, McGraw-Hill, ISBN 0-07-829744-3
  • Doug Smith: Digital Signal Processing Technology: Essentials of the Communications Revolution, American Radio Relay League, ISBN 0-87259-819-5
  • Smith, Steven W. (2002). Digital Signal Processing: A Practical Guide for Engineers and Scientists. Newnes. ISBN 0-7506-7444-X.
  • Stein, Jonathan Yaakov (2000-10-09). Digital Signal Processing, a Computer Science Perspective. Wiley. ISBN 0-471-29546-9.
  • Stergiopoulos, Stergios (2000). Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real-Time Systems. CRC Press. ISBN 0-8493-3691-0.
  • Van De Vegte, Joyce (2001). Fundamentals of Digital Signal Processing. Prentice Hall. ISBN 0-13-016077-6.
  • Oppenheim, Alan V.; Schafer, Ronald W. (2001). Discrete-Time Signal Processing. Pearson. ISBN 1-292-02572-7.
  • Hayes, Monson H. Statistical digital signal processing and modeling. John Wiley & Sons, 2009. (with MATLAB scripts)

References

[edit]
  1. ^ B. SOMANATHAN NAIR (2002). Digital electronics and logic design. PHI Learning Pvt. Ltd. p. 289. ISBN 9788120319561. Digital signals are fixed-width pulses, which occupy only one of two levels of amplitude.
  2. ^ Joseph Migga Kizza (2005). Computer Network Security. Springer Science & Business Media. ISBN 9780387204734.
  3. ^ 2000 Solved Problems in Digital Electronics. Tata McGraw-Hill Education. 2005. p. 151. ISBN 978-0-07-058831-8.
  4. ^ Billings, Stephen A. (Sep 2013). Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains. UK: Wiley. ISBN 978-1-119-94359-4.
  5. ^ Broesch, James D.; Stranneby, Dag; Walker, William (2008-10-20). Digital Signal Processing: Instant access (1 ed.). Butterworth-Heinemann-Newnes. p. 3. ISBN 9780750689762.
  6. ^ Srivastava, Viranjay M.; Singh, Ghanshyam (2013). MOSFET Technologies for Double-Pole Four-Throw Radio-Frequency Switch. Springer Science & Business Media. p. 1. ISBN 9783319011653.
  7. ^ Walden, R. H. (1999). "Analog-to-digital converter survey and analysis". IEEE Journal on Selected Areas in Communications. 17 (4): 539–550. doi:10.1109/49.761034.
  8. ^ Candes, E. J.; Wakin, M. B. (2008). "An Introduction To Compressive Sampling". IEEE Signal Processing Magazine. 25 (2): 21–30. Bibcode:2008ISPM...25...21C. doi:10.1109/MSP.2007.914731. S2CID 1704522.
  9. ^ a b c Marple, S. Lawrence (1987-01-01). Digital Spectral Analysis: With Applications. Englewood Cliffs, N.J: Prentice Hall. ISBN 978-0-13-214149-9.
  10. ^ a b c d Ribeiro, M.P.; Ewins, D.J.; Robb, D.A. (2003-05-01). "Non-stationary analysis and noise filtering using a technique extended from the original Prony method". Mechanical Systems and Signal Processing. 17 (3): 533–549. Bibcode:2003MSSP...17..533R. doi:10.1006/mssp.2001.1399. ISSN 0888-3270. Retrieved 2019-02-17.
  11. ^ So, Stephen; Paliwal, Kuldip K. (2005). "Improved noise-robustness in distributed speech recognition via perceptually-weighted vector quantisation of filterbank energies". Ninth European Conference on Speech Communication and Technology.
  12. ^ Mitrofanov, Georgy; Priimenko, Viatcheslav (2015-06-01). "Prony Filtering of Seismic Data". Acta Geophysica. 63 (3): 652–678. Bibcode:2015AcGeo..63..652M. doi:10.1515/acgeo-2015-0012. ISSN 1895-6572. S2CID 130300729.
  13. ^ Mitrofanov, Georgy; Smolin, S. N.; Orlov, Yu. A.; Bespechnyy, V. N. (2020). "Prony decomposition and filtering". Geology and Mineral Resources of Siberia (2): 55–67. doi:10.20403/2078-0575-2020-2-55-67. ISSN 2078-0575. S2CID 226638723. Retrieved 2020-09-08.
  14. ^ Gonzalez, Sira; Brookes, Mike (February 2014). "PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise". IEEE/ACM Transactions on Audio, Speech, and Language Processing. 22 (2): 518–530. doi:10.1109/TASLP.2013.2295918. ISSN 2329-9290. S2CID 13161793. Retrieved 2017-12-03.
  15. ^ Huang, N. E.; Shen, Z.; Long, S. R.; Wu, M. C.; Shih, H. H.; Zheng, Q.; Yen, N.-C.; Tung, C. C.; Liu, H. H. (1998-03-08). "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 454 (1971): 903–995. Bibcode:1998RSPSA.454..903H. doi:10.1098/rspa.1998.0193. ISSN 1364-5021. S2CID 1262186. Retrieved 2018-06-05.
  16. ^ Weipeng, Jiang; Zhiqiang, He; Ran, Duan; Xinglin, Wang (August 2012). "Major optimization methods for TD-LTE signal processing based on general purpose processor". 7th International Conference on Communications and Networking in China. pp. 797–801. doi:10.1109/ChinaCom.2012.6417593. ISBN 978-1-4673-2699-5. S2CID 17594911.
  17. ^ Zaynidinov, Hakimjon; Ibragimov, Sanjarbek; Tojiboyev, Gayrat; Nurmurodov, Javohir (2021-06-22). "Efficiency of Parallelization of Haar Fast Transform Algorithm in Dual-Core Digital Signal Processors". 2021 8th International Conference on Computer and Communication Engineering (ICCCE). IEEE. pp. 7–12. doi:10.1109/ICCCE50029.2021.9467190. ISBN 978-1-7281-1065-3. S2CID 236187914.
  18. ^ Lyakhov, P.A. (June 2023). "Area-Efficient digital filtering based on truncated multiply-accumulate units in residue number system 2 n - 1 , 2 n , 2 n + 1". Journal of King Saud University - Computer and Information Sciences. 35 (6): 101574. doi:10.1016/j.jksuci.2023.101574.
  19. ^ Stranneby, Dag; Walker, William (2004). Digital Signal Processing and Applications (2nd ed.). Elsevier. ISBN 0-7506-6344-8.
  20. ^ JPFix (2006). "FPGA-Based Image Processing Accelerator". Retrieved 2008-05-10.
  21. ^ Kapinchev, Konstantin; Bradu, Adrian; Podoleanu, Adrian (December 2019). "Parallel Approaches to Digital Signal Processing Algorithms with Applications in Medical Imaging". 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS) (PDF). pp. 1–7. doi:10.1109/ICSPCS47537.2019.9008720. ISBN 978-1-7281-2194-9. S2CID 211686462.
  22. ^ Rabiner, Lawrence R.; Gold, Bernard (1975). Theory and application of digital signal processing. Englewood Cliffs, NJ: Prentice-Hall, Inc. ISBN 978-0139141010.
  23. ^ Kerckhoff, Jessica; Listenberger, Jennifer; Valente, Michael (October 1, 2008). "Advances in hearing aid technology". Contemporary Issues in Communication Science and Disorders. 35: 102–112. doi:10.1044/cicsd_35_F_102.
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