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Noise (signal processing)

Noise (signal processing) is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Noise (signal processing) rather than just read about it. In short: In signal processing, noise is a general term for unwanted (and, in general, unknown) modifications that a signal may suffer during capture, storage, transmission, processing, or conversion. Sometimes the word is also used to mean signals that are random (unpredictable) and carry no useful information; even if they are not interfering with other signals or may have been introduced intentionally, as in comfort noise.

Key takeaways

  • Noise (signal processing) belongs to science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Noise (signal processing) to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Noise (signal processing) from memory before moving on to harder problems.

Reference excerpt

In signal processing, noise is a general term for unwanted (and, in general, unknown) modifications that a signal may suffer during capture, storage, transmission, processing, or conversion. Sometimes the word is also used to mean signals that are random (unpredictable) and carry no useful information; even if they are not interfering with other signals or may have been introduced intentionally, as in comfort noise. Noise reduction, the recovery of the original signal from the noise-corrupted one, is a very common goal in the design of signal processing systems, especially filters. The mathematical limits for noise removal are set by information theory.

Types of noise Signal processing noise can be classified by its statistical properties (sometimes called the "color" of the noise) and by how it modifies the intended signal:

Additive noise, gets added to the intended signal White noise Additive white Gaussian noise Black noise Gaussian noise Pink noise or flicker noise, with 1/f power spectrum Brownian noise, with 1/f2 power spectrum Contaminated Gaussian noise, whose PDF is a linear mixture of Gaussian PDFs Power-law noise Cauchy noise Multiplicative noise, multiplies or modulates the intended signal Quantization error, due to conversion from continuous to discrete values Poisson noise, typical of signals that are rates of discrete events Shot noise, e.g. caused by static electricity discharge Transient noise, a short pulse followed by decaying oscillations Burst noise, powerful but only during short intervals Phase noise, random time shifts in a signal

Noise in specific kinds of signals Noise may arise in signals of interest to various scientific and technical fields, often with specific features:

Noise (audio), such as "hiss" or "hum", in audio signals Background noise, due to spurious sounds during signal capture Comfort noise, added to voice communications to fill silent gaps Electromagnetically induced noise, audible noise due to electromagnetic vibrations in systems involving electromagnetic fields Noise (video), such as "snow" Noise (radio), such as "static", in radio transmissions Image noise, affects images, usually digital ones Salt and pepper noise or spike noise, scattered very dark or very light pixels Fixed pattern noise, tied to pixel sensors Shadow noise, made visible by increasing brightness or contrast Speckle noise, typical of radar imaging and interferograms Film grain in analog photography Compression artifacts or "mosquito noise" around edges in JPEG and other formats Noise (electronics) in electrical signals Johnson–Nyquist noise, in semiconductors Quantum noise Quantum 1/f noise, a disputed theory about quantum systems Generation-recombination noise, in semiconductor devices Oscillator phase noise, random fluctuations of the phase of an oscillator Barkhausen effect or Barkhausen noise, in the strength of a ferromagnet Spectral splatter or switch noise, caused by on/off transmitter switching Ground noise, appearing at the ground terminal of audio equipment Synaptic noise, observed in neuroscience Neuronal noise, observed in neuroscience Transcriptional noise in the transcription of genes to proteins Cosmic noise, in radioastronomy Phonon noise in materials science Internet background noise, packets sent to unassigned or inactive IP addresses Fano noise, in particle detectors Mode partition noise in optical cables Seismic noise, spurious ground vibrations in seismology Cosmic microwave background, microwave noise left over from the Big Bang

Measures of noise in signals A long list of noise measures has been defined to measure noise in signal processing: in absolute terms, relative to some standard noise level, or relative to the desired signal level. They include:

Dynamic range, often defined by inherent noise level Signal-to-noise ratio (SNR), ratio of noise power to signal power Peak signal-to-noise ratio, maximum SNR in a system Signal to noise ratio (imaging), for images Carrier-to-noise ratio, the signal-to-noise ratio of a modulated signal Noise power Noise figure Noise-equivalent flux density, a measure of noise in astronomy Noise floor Noise margin, by how much a signal exceeds the noise level Reference noise, a reference level for electronic noise Noise spectral density, noise power per unit of bandwidth Noise temperature Effective input noise temperature Noise-equivalent power, a measure of sensitivity for photodetectors Relative intensity noise, in a laser beam Antenna noise temperature, measure of noise in telecommunications antenna Received noise power, noise at a telecommunications receiver Circuit noise level, ratio of circuit noise to some reference level Channel noise level, some measure of noise in a communication channel Noise-equivalent target, intensity of a target when the signal-to-noise level is 1 Equivalent noise resistance, a measure of noise based on an equivalent resistor Carrier-to-receiver noise density, ratio of received carrier power to receiver noise Carrier-to-noise-density ratio, Spectral signal-to-noise ratio Antenna gain-to-noise temperature, a measure of antenna performance Contrast-to-noise ratio, a measure of image quality Noise print, statistical signature of ambient noise for its suppression Equivalent pulse code modulation noise, measure of noise by comparing to PCM quantization noise

Technology for noise in signals Almost every technique and device for signal processing has some connection to noise. Some random examples are:

Noise shaping Antenna analyzer or noise bridge, used to measure the efficiency of antennas Noise gate Noise generator, a circuit that produces a random electrical signal Radio noise source used to calibrate radiotelescopes Friis formulas for noise in telecommunications Noise-domain reflectometry, uses existing signals to find cable faults Noise-immune cavity-enhanced optical heterodyne molecular spectroscopy

See also Noise (electronics) Signal-to-noise statistic, a mathematical formula to measure the difference of two values relative to their standard deviations

References

Worked examples

Example 1 — a first encounter with Noise (signal processing)

Start with the simplest possible case. Write down what Noise (signal processing) claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Noise (signal processing) before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Noise (signal processing) ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Noise (signal processing)

In research
Noise (signal processing) appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Noise (signal processing) in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Noise (signal processing) is common in secondary-school and first-year university syllabi. It links to neighbouring topics Signal processing, so understanding it makes those chapters shorter.
In everyday life
Look for Noise (signal processing) outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Noise (signal processing) in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Noise (signal processing) means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Noise (signal processing) out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Noise (signal processing) in simple terms?

In signal processing, noise is a general term for unwanted (and, in general, unknown) modifications that a signal may suffer during capture, storage, transmission, processing, or conversion. Sometimes the word is also used to mean signals that are random (unpredictable) and carry no useful informat…

Why does Noise (signal processing) matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Noise (signal processing)?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Noise (signal processing).

Tags

  • Signal processing

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