In signal theory, the noise floor is the measure of the signal created from the sum of all the noise sources and unwanted signals within a measurement system, where noise is defined as any signal other than the one being monitored. In radio communication and electronics, this may include thermal noise, black body, cosmic noise as well as atmospheric noise from distant thunderstorms and similar and any other unwanted man-made signals, sometimes referred to as incidental noise. If the dominant noise is generated within the measuring equipment (for example by a receiver with a poor noise figure) then this is an example of an instrumentation noise floor, as opposed to a physical noise floor. These terms are not always clearly defined, and are sometimes confused. Avoiding interference between electrical systems is the distinct subject of electromagnetic compatibility (EMC). In a measurement system such as a seismograph, the physical noise floor may be set by the incidental noise, and may include nearby foot traffic or a nearby road. The noise floor limits the smallest measurement that can be taken with certainty since any measured amplitude can on average be no less than the noise floor. A common way to lower the noise floor in electronics systems is to cool the system to reduce thermal noise, when this is the major noise source. In special circumstances, the noise floor can also be artificially lowered with digital signal processing techniques. Signals that are below the noise floor can be detected by using different techniques of spread spectrum communications, where signal of a particular information bandwidth is deliberately spread in the frequency domain resulting in a signal with a wider occupied bandwidth. Every additional 6.02 dB of noise floor corresponds to a 1-bit reduction of the effective number of bits of an analog-to-digital converter or digital-to-analog converter.

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Noise (signal processing)
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.
Audio bit depth
In digital audio using pulse-code modulation (PCM), bit depth is the number of bits of information in each sample, and it directly corresponds to the resolution of each sample. Examples of bit depth include Compact Disc Digital Audio, which uses 16 bits per sample, and DVD-Audio and Blu-ray Disc which can support up to 24 bits per sample. In basic implementations, variations in bit depth primarily affect the noise level from quantization error—thus the signal-to-noise ratio (SNR) and dynamic range.
Dynamic range
Dynamic range (abbreviated DR, DNR, or DYR) is the ratio between the largest and smallest values that a certain quantity can assume. It is often used in the context of signals, like sound and light. It is measured either as a ratio or as a base-10 (decibel) or base-2 (doublings, bits or stops) logarithmic value of the difference between the smallest and largest signal values. Electronically reproduced audio and video is often processed to fit the original material with a wide dynamic range into a narrower recorded dynamic range that can more easily be stored and reproduced; this processing is called dynamic range compression.
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