# Signal processing

**signal analysissignalsignal processorstatistical signal processingprocessingsignalsanalysis and signal processingsignal theorysignal-processingdigital signal processing**

Signal processing is a subfield of mathematics, information and electrical engineering that concerns the analysis, synthesis, and modification of signals, which are broadly defined as functions conveying "information about the behavior or attributes of some phenomenon", such as sound, images, and biological measurements.wikipedia

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### Signal

**signalselectrical signalelectrical signals**

Signal processing is a subfield of mathematics, information and electrical engineering that concerns the analysis, synthesis, and modification of signals, which are broadly defined as functions conveying "information about the behavior or attributes of some phenomenon", such as sound, images, and biological measurements.

In communication systems, signal processing, and electrical engineering, a signal is a function that "conveys information about the behavior or attributes of some phenomenon".

### Audio signal processing

**audio processingaudio processorsound processing**

Signal processing is a subfield of mathematics, information and electrical engineering that concerns the analysis, synthesis, and modification of signals, which are broadly defined as functions conveying "information about the behavior or attributes of some phenomenon", such as sound, images, and biological measurements.

Audio signal processing is a subfield of signal processing that is concerned with the electronic manipulation of audio signals.

### Electrical engineering

**electrical engineerelectricalelectrical engineers**

Signal processing is a subfield of mathematics, information and electrical engineering that concerns the analysis, synthesis, and modification of signals, which are broadly defined as functions conveying "information about the behavior or attributes of some phenomenon", such as sound, images, and biological measurements.

Electrical engineering has now subdivided into a wide range of subfields including electronics, digital computers, computer engineering, power engineering, telecommunications, control systems, radio-frequency engineering, signal processing, instrumentation, and microelectronics.

### Information engineering (field)

**information engineeringInformationIE/Information engineering**

The components of information engineering include more theoretical fields such as machine learning, artificial intelligence, control theory, signal processing, and information theory, and more applied fields such as computer vision, natural language processing, bioinformatics, medical image computing, cheminformatics, autonomous robotics, mobile robotics, and telecommunications.

### Digital image processing

**image processingimageprocessing**

Multi-scale signal analysis

### Wiener filter

**WienerWiener–Kolmogorov filterWiener filtering**

Examples of algorithms are the Fast Fourier transform (FFT), finite impulse response (FIR) filter, Infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters.

In signal processing, the Wiener filter is a filter used to produce an estimate of a desired or target random process by linear time-invariant (LTI) filtering of an observed noisy process, assuming known stationary signal and noise spectra, and additive noise.

### Kalman filter

**unscented Kalman filterKalmanInformation filter**

Examples of algorithms are the Fast Fourier transform (FFT), finite impulse response (FIR) filter, Infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters.

Furthermore, the Kalman filter is a widely applied concept in time series analysis used in fields such as signal processing and econometrics.

### Electronic mixer

**mixermixersmixing**

The former are, for instance, passive filters, active filters, additive mixers, integrators and delay lines.

With appropriate signal analysis the phase of the signal can be recovered as well.

### Video processing

**video processorprocessingvideo**

Video processing – for interpreting moving pictures

In electronics engineering, video processing is a particular case of signal processing, in particular image processing, which often employs video filters and where the input and output signals are video files or video streams.

### Array processing

**array signal processingarrays**

Array processing – for processing signals from arrays of sensors

Array processing: signal processing is a wide area of research that extends from the simplest form of 1-D signal processing to the complex form of M-D and array signal processing.

### Alan V. Oppenheim

**A. V. Oppenheim**

According to Alan V. Oppenheim and Ronald W. Schafer, the principles of signal processing can be found in the classical numerical analysis techniques of the 17th century.

### Financial signal processing

**financial trading signals**

Financial signal processing – analyzing financial data using signal processing techniques, especially for prediction purposes.

Financial signal processing is a branch of signal processing technologies which applies to financial signals.

### Data compression

**compressionvideo compressioncompressed**

(Source coding), including audio compression, image compression, and video compression.

In signal processing, data compression, source coding, or bit-rate reduction involves encoding information using fewer bits than the original representation.

### Filter (signal processing)

**filterfiltersfiltering**

Filters – for example analog (passive or active) or digital (FIR, IIR, frequency domain or stochastic filters, etc.)

In signal processing, a filter is a device or process that removes some unwanted components or features from a signal.

### Finite impulse response

**FIRFIR filterFinite Impulse Response (FIR)**

Examples of algorithms are the Fast Fourier transform (FFT), finite impulse response (FIR) filter, Infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters. Filters – for example analog (passive or active) or digital (FIR, IIR, frequency domain or stochastic filters, etc.)

In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time.

### Sampling (signal processing)

**sampling ratesamplingsample rate**

Samplers and analog-to-digital converters 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.

In signal processing, sampling is the reduction of a continuous-time signal to a discrete-time signal.

### Integrator

**current integratorintegratorstime integrator**

The former are, for instance, passive filters, active filters, additive mixers, integrators and delay lines.

Signal processing

### Time–frequency analysis

**time-frequency analysistime-frequency domainfrequency-time**

Time-frequency analysis – for processing non-stationary signals

In signal processing, time–frequency analysis comprises those techniques that study a signal in both the time and frequency domains simultaneously, using various time–frequency representations.

### Computer vision

**visionimage classificationImage recognition**

Feature extraction, such as image understanding and speech recognition.

Yet another field related to computer vision is signal processing.

### Spectral density estimation

**spectral estimationfrequency estimationspectral analysis**

Spectral estimation – for determining the spectral content (i.e., the distribution of power over frequency) of a time series

In statistical signal processing, the goal of spectral density estimation (SDE) is to estimate the spectral density (also known as the power spectral density) of a random signal from a sequence of time samples of the signal.

### Linear time-invariant system

**linear time-invariantLTIlinear time invariant**

Linear time-invariant system theory, and transform theory

Linear time-invariant theory, commonly known as LTI system theory, comes from applied mathematics and has direct applications in NMR spectroscopy, seismology, circuits, signal processing, control theory, and other technical areas.

### Physical layer

**physicalPHYlayer 1**

OSI layer 1 in the seven layer OSI model, the Physical Layer (modulation, equalization, multiplexing, etc.);

Equalization filtering, training sequences, pulse shaping and other signal processing of physical signals

### Stochastic process

**stochastic processesrandom processstochastic**

Statistical signal processing is an approach which treats signals as stochastic processes, utilizing their statistical properties to perform signal processing tasks.

They have applications in many disciplines including sciences such as biology, chemistry, ecology, neuroscience, and physics as well as technology and engineering fields such as image processing, signal processing, information theory, computer science, cryptography and telecommunications.

### Time series

**time series analysistime-seriestime-series analysis**

Time series

Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.

### Companding

**compandercompandedcompandor**

Non-linear circuits include compandors, multiplicators (frequency mixers and voltage-controlled amplifiers), voltage-controlled filters, voltage-controlled oscillators and phase-locked loops.

In telecommunication and signal processing companding (occasionally called compansion) is a method of mitigating the detrimental effects of a channel with limited dynamic range.