Decibels

Because many signals have a very wide dynamic range, signals are often expressed using the logarithmic decibel scale. Based upon the definition of decibel, signal and noise may be expressed in decibels (dB) as

${\displaystyle P_{\mathrm {signal,dB} }=10\log _{10}\left(P_{\mathrm {signal} }\right)}$

and

${\displaystyle P_{\mathrm {noise,dB} }=10\log _{10}\left(P_{\mathrm {noise} }\right).}$

In a similar manner, SNR may be expressed in decibels as

${\displaystyle \mathrm {SNR_{dB}} =10\log _{10}\left(\mathrm {SNR} \right).}$

Using the definition of SNR

${\displaystyle \mathrm {SNR_{dB}} =10\log _{10}\left({\frac {P_{\mathrm {signal} }}{P_{\mathrm {noise} }}}\right).}$

Using the quotient rule for logarithms

${\displaystyle 10\log _{10}\left({\frac {P_{\mathrm {signal} }}{P_{\mathrm {noise} }}}\right)=10\log _{10}\left(P_{\mathrm {signal} }\right)-10\log _{10}\left(P_{\mathrm {noise} }\right).}$

Substituting the definitions of SNR, signal, and noise in decibels into the above equation results in an important formula for calculating the signal to noise ratio in decibels, when the signal and noise are also in decibels:

${\displaystyle \mathrm {SNR_{dB}} ={P_{\mathrm {signal,dB} }-P_{\mathrm {noise,dB} }}.}$

In the above formula, P is measured in units of power, such as watts (W) or milliwatts (mW), and the signal-to-noise ratio is a pure number.

However, when the signal and noise are measured in volts (V) or amperes (A), which are measures of amplitude,^{[note 1]} they must first be squared to obtain a quantity proportional to power, as shown below:

${\displaystyle \mathrm {SNR_{dB}} =10\log _{10}\left[\left({\frac {A_{\mathrm {signal} }}{A_{\mathrm {noise} }}}\right)^{2}\right]=20\log _{10}\left({\frac {A_{\mathrm {signal} }}{A_{\mathrm {noise} }}}\right)=2\left({A_{\mathrm {signal,dB} }-A_{\mathrm {noise,dB} }}\right).}$

Dynamic range

The concepts of signal-to-noise ratio and dynamic range are closely related. Dynamic range measures the ratio between the strongest un-distorted signal on a channel and the minimum discernible signal, which for most purposes is the noise level. SNR measures the ratio between an arbitrary signal level (not necessarily the most powerful signal possible) and noise. Measuring signal-to-noise ratios requires the selection of a representative or reference signal. In audio engineering, the reference signal is usually a sine wave at a standardized nominal or alignment level, such as 1 kHz at +4 dBu (1.228 V_RMS).

SNR is usually taken to indicate an average signal-to-noise ratio, as it is possible that instantaneous signal-to-noise ratios will be considerably different. The concept can be understood as normalizing the noise level to 1 (0 dB) and measuring how far the signal 'stands out'.

Difference from conventional power

In physics, the average power of an AC signal is defined as the average value of voltage times current; for resistive (non-reactive) circuits, where voltage and current are in phase, this is equivalent to the product of the rms voltage and current:

${\displaystyle \mathrm {P} =V_{\mathrm {rms} }I_{\mathrm {rms} }}$

${\displaystyle \mathrm {P} ={\frac {V_{\mathrm {rms} }^{2}}{R}}=I_{\mathrm {rms} }^{2}R}$

But in signal processing and communication, one usually assumes that ${\displaystyle R=1\Omega }$ ^[3] so that factor is usually not included while measuring power or energy of a signal. This may cause some confusion among readers, but the resistance factor is not significant for typical operations performed in signal processing, or for computing power ratios. For most cases, the power of a signal would be considered to be simply

${\displaystyle \mathrm {P} =V_{\mathrm {rms} }^{2}}$

Amplitude modulation

Channel signal-to-noise ratio is given by

${\displaystyle \mathrm {(SNR)_{C,AM}} ={\frac {A_{C}^{2}(1+k_{a}^{2}P)}{2WN_{0}}}}$

where W is the bandwidth and ${\displaystyle k_{a}}$ is modulation index

Output signal-to-noise ratio (of AM receiver) is given by

${\displaystyle \mathrm {(SNR)_{O,AM}} ={\frac {A_{c}^{2}k_{a}^{2}P}{2WN_{0}}}}$

Frequency modulation

Channel signal-to-noise ratio is given by

${\displaystyle \mathrm {(SNR)_{C,FM}} ={\frac {A_{c}^{2}}{2WN_{0}}}}$

Output signal-to-noise ratio is given by

${\displaystyle \mathrm {(SNR)_{O,FM}} ={\frac {A_{c}^{2}k_{f}^{2}P}{2N_{0}W^{3}}}}$

Fixed point

For n-bit integers with equal distance between quantization levels (uniform quantization) the dynamic range (DR) is also determined.

Assuming a uniform distribution of input signal values, the quantization noise is a uniformly distributed random signal with a peak-to-peak amplitude of one quantization level, making the amplitude ratio 2ⁿ/1. The formula is then:

${\displaystyle \mathrm {DR_{dB}} =\mathrm {SNR_{dB}} =20\log _{10}(2^{n})\approx 6.02\cdot n}$

This relationship is the origin of statements like "16-bit audio has a dynamic range of 96 dB". Each extra quantization bit increases the dynamic range by roughly 6 dB.

Assuming a full-scale sine wave signal (that is, the quantizer is designed such that it has the same minimum and maximum values as the input signal), the quantization noise approximates a sawtooth wave with peak-to-peak amplitude of one quantization level^[11] and uniform distribution. In this case, the SNR is approximately

${\displaystyle \mathrm {SNR_{dB}} \approx 20\log _{10}(2^{n}{\textstyle {\sqrt {3/2}}})\approx 6.02\cdot n+1.761}$

Floating point

Floating-point numbers provide a way to trade off signal-to-noise ratio for an increase in dynamic range. For n bit floating-point numbers, with n-m bits in the mantissa and m bits in the exponent:

${\displaystyle \mathrm {DR_{dB}} =6.02\cdot 2^{m}}$

${\displaystyle \mathrm {SNR_{dB}} =6.02\cdot (n-m)}$

Note that the dynamic range is much larger than fixed-point, but at a cost of a worse signal-to-noise ratio. This makes floating-point preferable in situations where the dynamic range is large or unpredictable. Fixed-point's simpler implementations can be used with no signal quality disadvantage in systems where dynamic range is less than 6.02m. The very large dynamic range of floating-point can be a disadvantage, since it requires more forethought in designing algorithms.^{[12][note 3][note 4]}