Digital signal processing

DSPsignal processingdigitaldigital signal processing (DSP)Digital Signal Processorsdigital transformDSP (Digital Signal Processing)processingsignalspatial domain
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.wikipedia
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Digital signal processor

DSPDSPsdigital signal processors
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.
A digital signal processor (DSP) is a specialized microprocessor (or a SIP block) chip, with its architecture optimized for the operational needs of digital signal processing.

Analog signal processing

analoganalog-control voltages
Digital signal processing and analog signal processing are subfields of signal processing.
Analog signal processing is a type of signal processing conducted on continuous analog signals by some analog means (as opposed to the discrete digital signal processing where the signal processing is carried out by a digital process).

Speech processing

speech signal processingspeechmachine processing of speech
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. Applications of DSP include audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, digital synthesizers, radar, sonar, financial signal processing, seismology and biomedicine.
The signals are usually processed in a digital representation, so speech processing can be regarded as a special case of digital signal processing, applied to speech signals.

Digital image processing

image processingimageprocessing
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. Applications of DSP include audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, digital synthesizers, radar, sonar, financial signal processing, seismology and biomedicine.
As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing.

Radar

radar stationradarsradar system
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. Applications of DSP include audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, digital synthesizers, radar, sonar, financial signal processing, seismology and biomedicine.
High tech radar systems are associated with digital signal processing, machine learning and are capable of extracting useful information from very high noise levels.

Nyquist–Shannon sampling theorem

sampling theoremNyquist-Shannon sampling theoremNyquist theorem
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 the field of digital signal processing, the sampling theorem is a fundamental bridge between continuous-time signals and discrete-time signals.

Quantization (signal processing)

quantizationquantization errorquantized
Sampling is usually carried out in two stages, discretization and quantization. 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.
Quantization, in mathematics and digital signal processing, is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite number of elements.

Discrete Fourier transform

DFTcircular convolution theoremFourier transform
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.
In digital signal processing, the function is any quantity or signal that varies over time, such as the pressure of a sound wave, a radio signal, or daily temperature readings, sampled over a finite time interval (often defined by a window function ).

Telecommunication

telecommunicationscommunicationstelecom
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.
There was a rapid growth of the telecommunications industry towards the end of the 20th century, driven by the development of metal-oxide-semiconductor (MOS) large-scale integration (LSI) technology, information theory, digital signal processing, and wireless communications such as cellular networks and mobile telephony.

Audio signal processing

audio processoraudio processingsound 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. Applications of DSP include audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, digital synthesizers, radar, sonar, financial signal processing, seismology and biomedicine.

Sampling (signal processing)

sampling ratesamplingsample rate
The 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. 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.
However, digital signal processing operations can have very high dynamic range, consequently it is common to perform mixing and mastering operations at 32-bit precision and then convert to 16- or 24-bit for distribution.

Spectral density estimation

spectral estimationfrequency estimationspectral analysis
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.
Frequency estimation is the process of estimating the complex frequency components of a signal in the presence of noise given assumptions about the number of the components.

Discrete time and continuous time

discrete timediscrete-timecontinuous-time
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.
Discrete-time signals, used in digital signal processing, can be obtained by sampling and quantization of continuous signals.

Digital synthesizer

Digitaldigital synthesisdigital synthesizers
Applications of DSP include audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, digital synthesizers, radar, sonar, financial signal processing, seismology and biomedicine.
A digital synthesizer is a synthesizer that uses digital signal processing (DSP) techniques to make musical sounds.

Convolution

convolvedconvolvingconvolution kernel
The output of a linear digital filter to any given input may be calculated by convolving the input signal with the impulse response.
Digital signal processing and other applications typically use fast convolution algorithms to reduce the cost of the convolution to O(N log N) complexity.

Impulse response

impulseImpulse Response Functionsimpulse-response function.
The output of a linear digital filter to any given input may be calculated by convolving the input signal with the impulse response. A filter may also be described as a difference equation, a collection of zeros and poles or an impulse response or step response.
Impulse response analysis is a major facet of radar, ultrasound imaging, and many areas of digital signal processing.

Mobile phone

cell phonemobile phonesmobile
Specific examples include speech coding and transmission in digital mobile phones, room correction of sound in hi-fi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes, medical imaging such as CAT scans and MRI, MP3 compression, computer graphics, image manipulation, audio crossovers and equalization, and audio effects units.
Digital cellular networks appeared in the 1990s, enabled by the wide adoption of MOSFET-based RF power amplifiers (power MOSFET and LDMOS) and RF circuits (RF CMOS), leading to the introduction of digital signal processing in wireless communications.

Frequency domain

frequency-domainFourier spaceFourier domain
Nonlinear signal processing is closely related to nonlinear system identification and can be implemented in the time, frequency, and spatio-temporal domains.

Stream processing

stream processorsstream processorstream programming
Additional technologies for digital signal processing include more powerful general purpose microprocessors, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial applications such as motor control), and stream processors.
Stream processing is essentially a compromise, driven by a data-centric model that works very well for traditional DSP or GPU-type applications (such as image, video and digital signal processing) but less so for general purpose processing with more randomized data access (such as databases).

Bilinear transform

bilinearitytransform
The bilinear transform (also known as Tustin's method) is used in digital signal processing and discrete-time control theory to transform continuous-time system representations to discrete-time and vice versa.

Discrete cosine transform

DCTiDCTinverse discrete cosine transform
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.
DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, communications devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations.

Goertzel algorithm

The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of the individual terms of the discrete Fourier transform (DFT).

Recurrence relation

difference equationdifference equationsrecurrence relations
A filter may also be described as a difference equation, a collection of zeros and poles or an impulse response or step response.
In digital signal processing, recurrence relations can model feedback in a system, where outputs at one time become inputs for future time.

Analog-to-digital converter

ADCanalog to digital converteranalog-to-digital conversion
To digitally analyze and manipulate an analog signal, it must be digitized with an analog-to-digital converter (ADC).

Equalization (audio)

equalizationequalizerEQ
Specific examples include speech coding and transmission in digital mobile phones, room correction of sound in hi-fi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes, medical imaging such as CAT scans and MRI, MP3 compression, computer graphics, image manipulation, audio crossovers and equalization, and audio effects units.
In the late 1990s and in the 2000s, parametric equalizers became increasingly available as Digital Signal Processing (DSP) equipment, usually in the form of plug-ins for various digital audio workstations.