By Marina Bosi, Richard E. Goldberg (auth.)

**Introduction to electronic Audio Coding and Standards** offers a close advent to the equipment, implementations, and reputable criteria of state of the art audio coding expertise. within the ebook, the speculation and implementation of every of the elemental coder development blocks is addressed. The construction blocks are then healthy jointly right into a complete coder and the reader is proven how you can pass judgement on the functionality of this sort of coder. eventually, the authors speak about the good points, offerings, and function of the most cutting-edge coders outlined within the ISO/IEC MPEG and HDTV criteria and in advertisement use this day.

The final aim of this booklet is to give the reader with a fantastic adequate figuring out of the most important concerns within the idea and implementation of perceptual audio coders that they're in a position to construct their very own basic audio codec. there is not any different resource on hand the place a non-professional has entry to the genuine secrets and techniques of audio coding.

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In this section, we discuss the basics of quantization technology. In particular, we focus on instantaneous or scalar quantization, where the mapping of an amplitude value is not largely influenced by previous or following amplitude values. This is not the case, for example, in "vector quantization" systems. In vector Chapter 2: Quantization 21 quantization a group of consecutive amplitude values are quantized into a single code. As we shall see later in this chapter when we discuss Huffman coding, this can give coding gain when there are strong temporal correlations between consecutive amplitude values.

In general, the signal to noise ratio, SNR, will depend on the number of mantissa bits and will stay roughly constant over the whole range of input signal powers. This contrasts with uniform quantization where the SNR is highest at high powers (but low enough to avoid clipping) and decreases as the signal power decreases. We see examples of this in the next section. You can peek ahead to Figure /0 to see a graph of this behavior. We describe here a particular implementation of floating point quantization that is very similar to linearized A-law companding as was specified by the CCITT based on [Jayant and Noll 84].

Dequantization then is carried out by first dequantizing the uniformly quantized code into a value y' and then passing that value through the inverse function Chapter 2: Quantization 27 The function c(x) is normally anti-symmetric around x=O so that it maps negative values of x onto negative values. This implies that we can fully define c(x) if we specify c(ixl). 0. The requirement that c(x) be monotonically increasing is so that c(x) is easily invertible. To get a feel for how companding affects quantization, let's see how the size of the quantizer bins varies with input level x.