U-Law


The µ-law algorithm (sometimes written "mu-law", or approximated as "u-law") is a companding algorithm, primarily used in the digital telecommunication systems of North America and Japan. Companding algorithms reduce the dynamic range of an audio signal. In analog systems, this can increase the signal-to-noise ratio (SNR) achieved during transmission, and in the digital domain, it can reduce the quantization error (hence increasing signal to quantization noise ratio). These SNR increases can be traded instead for reduced bandwidth for equivalent SNR.

It is similar to the A-law algorithm used in regions where digital telecommunication signals are carried on E-1 circuits, e.g. Europe.

Algorithm types

There are two forms of this algorithm—an analog version, and a quantized digital version.

Continuous

For a given input Template:Mvar, the equation for μ-law encoding is[1]

F(x) = \sgn(x) \frac{\ln(1+ \mu |x|)}{\ln(1+\mu)}~~~~-1 \leq x \leq 1

where μ = 255 (8 bits) in the North American and Japanese standards. It is important to note that the range of this function is −1 to 1.

μ-law expansion is then given by the inverse equation:

F^{-1}(y) = \sgn(y) (1 / \mu ) ((1 + \mu)^{|y|}- 1)~~~~-1 \leq y \leq 1

The equations are culled from Cisco's Waveform Coding Techniques.

Discrete

This is defined in ITU-T Recommendation G.711.[2]

G.711 is unclear about how to code the values at the limit of a range (e.g. whether +31 codes to 0xEF or 0xF0). However, G.191 provides example C code for a μ-law encoder which gives the following encoding. Note the difference between the positive and negative ranges, e.g. the negative range corresponding to +30 to +1 is −31 to −2. This is accounted for by the use of 1's complement (simple bit inversion) rather than 2's complement to convert a negative value to a positive value during encoding.

Quantized μ-law algorithm
14 bit Binary Linear input code 8 bit Compressed code
+8158 to +4063 in 16 intervals of 256 0x80 + interval number
+4062 to +2015 in 16 intervals of 128 0x90 + interval number
+2014 to +991 in 16 intervals of 64 0xA0 + interval number
+990 to +479 in 16 intervals of 32 0xB0 + interval number
+478 to +223 in 16 intervals of 16 0xC0 + interval number
+222 to +95 in 16 intervals of 8 0xD0 + interval number
+94 to +31 in 16 intervals of 4 0xE0 + interval number
+30 to +1 in 15 intervals of 2 0xF0 + interval number
0 0xFF
−1 0x7F
−31 to −2 in 15 intervals of 2 0x70 + interval number
−95 to −32 in 16 intervals of 4 0x60 + interval number
−223 to −96 in 16 intervals of 8 0x50 + interval number
−479 to −224 in 16 intervals of 16 0x40 + interval number
−991 to −480 in 16 intervals of 32 0x30 + interval number
−2015 to −992 in 16 intervals of 64 0x20 + interval number
−4063 to −2016 in 16 intervals of 128 0x10 + interval number
−8159 to −4064 in 16 intervals of 256 0x00 + interval number

Implementation

There are three ways of implementing a μ-law algorithm:

Analog
Use an amplifier with non-linear gain to achieve companding entirely in the analog domain.
Non-linear ADC
Use an analog-to-digital converter with quantization levels which are unequally spaced to match the μ-law algorithm.
Digital
Use the quantized digital version of the μ-law algorithm to convert data once it is in the digital domain.

Usage justification

This encoding is used because speech has a wide dynamic range. In the analog world, when mixed with a relatively constant background noise source, the finer detail is lost. Given that the precision of the detail is compromised anyway, and assuming that the signal is to be perceived as audio by a human, one can take advantage of the fact that perceived intensity (loudness) is logarithmic by compressing the signal using a logarithmic-response op-amp (Weber-Fechner law). In telco circuits, most of the noise is injected on the lines, thus after the compressor, the intended signal will be perceived as significantly louder than the static, compared to an un-compressed source. This became a common telco solution, and thus, prior to common digital usage, the μ-law specification was developed to define an inter-compatible standard.

As the digital age dawned, it was noted that this pre-existing algorithm had the effect of significantly reducing the number of bits needed to encode recognizable human voice. Using μ-law, a sample could be effectively encoded in as few as 8 bits, a sample size that conveniently matched the symbol size of most standard computers.

μ-law encoding effectively reduced the dynamic range of the signal, thereby increasing the coding efficiency while biasing the signal in a way that results in a signal-to-distortion ratio that is greater than that obtained by linear encoding for a given number of bits. This is an early form of perceptual audio encoding.

The μ-law algorithm is also used in the .au format, which dates back at least to the SPARCstation 1 as the native method used by Sun's /dev/audio interface, widely used as a de facto standard for Unix sound. The .au format is also used in various common audio APIs such as the classes in the sun.audio Java package in Java 1.1 and in some C# methods.

This plot illustrates how μ-law concentrates sampling in the smaller (softer) values. The values of a μ-law byte 0-255 are the horizontal axis, the vertical axis is the 16 bit linear decoded value. This image was generated with the Sun Microsystems c routine g711.c commonly available on the Internet.

File:Ulaw.JPG

Comparison with A-law

The µ-law algorithm provides a slightly larger dynamic range than the A-law at the cost of worse proportional distortion for small signals. By convention, A-law is used for an international connection if at least one country uses it.

See also

References

 This article incorporates "Federal Standard 1037C".

External links

  • Waveform Coding Techniques – details of implementation
  • PDF)
  • PDF)
  • A-law and μ-law realisation (in C) (ctrl-a "highlight all" to see linked black-on-black text).
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