By Yuli You
Audio Coding: concept and functions offers succinct insurance of audio coding applied sciences which are prevalent in sleek audio coding criteria. added from the point of view of an engineer, this booklet articulates how sign processing is utilized in the context of audio coding. It provides an in depth remedy of up to date audio coding applied sciences after which makes use of the DRA audio coding commonplace as a pragmatic instance to demonstrate how various applied sciences are built-in right into a fully-fledged audio coding set of rules. Drawing upon years of sensible adventure and utilizing quite a few examples and illustrations Dr. Yuli You, supplies an outline of functional audio coding applied sciences together with: • Designing high-performance algorithms that may be effortlessly applied on fixed-point or integer microprocessors. • the best way to adequately enforce an audio decoder on numerous microprocessors. temporary detection and edition of time-frequency solution of subband filters. • Psychoacoustic versions and optimum bit allocation. Audio Coding: conception and functions should be a beneficial reference ebook for engineers within the buyer electronics undefined, in addition to scholars and researchers in electric engineering.
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Additional resources for Audio Coding: Theory and Applications
Piece-wise linear approximation may lead to degradation in audio quality, hence may be unacceptable for high fidelity application. Another alternative is to store the exponential function as a quantization table. This amounts to 13 3 D 39 KB if each of the 213 entries in the table are stored using 24 bits. The most widely used companding in audio coding is the companding of quantization step sizes of uniform quantizers. Since quantization step sizes are needed in the inverse quantization process in the decoder, they need to be packed into the bit stream and transmitted to the decoder.
This leads to the Lloyd-Max algorithm. Another approach is to transform the source signal through a nonlinear function in such a way that the transformed signal has a PDF that is almost uniform, then a uniform quantizer may be used to deliver improved performance. This leads to companding. 10) is minimized. x/dx 1 which indicates that the quantized value for each decision interval is the centroid of the probability mass in the interval. 52) which indicates that the decision boundary is simply the midpoint of the neighboring quantized values.
Even with uncorrelated sequences, VQ can still achieve better performance than SQ. 3 plots an independent Gaussian sequence (meanD0, varianceD1) over the decision boundaries (solid lines) and quantized values (dashed lines) of the same midtread uniform SQ. Apparently the samples are still concentrated, even though not as much as the correlated sequence in Fig. 2, so a VQ can still achieve better performance than an SQ. At least, an SQ has to allocate decision squares to cover the four corners of the figure, but a VQ can use arbitrarily shaped decision regions to cover those areas without wasting bits.