Download Audio Signal Processing and Coding by Andreas Spanias PDF

By Andreas Spanias

An in-depth remedy of algorithms and criteria for perceptual coding of high-fidelity audio, this self-contained reference surveys and addresses all facets of the sphere. insurance comprises sign processing and perceptual (psychoacoustic) basics, information on correct examine and sign versions, info on standardization and purposes, and info on functionality measures and perceptual dimension structures. It features a entire bibliography with over six hundred references, laptop routines, and MATLAB-based tasks to be used in EE multimedia, desktop technology, and DSP courses.  An ftp website containing supplementary fabric resembling wave records, MATLAB courses and workspaces for the scholars to unravel a number of the numerical difficulties and machine routines within the e-book are available at

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Sampling rate changes have been popular in DSP and audio applications. Depending on the application, changes in the sampling rate may reduce algorithmic and hardware complexity or increase resolution in certain signal processing operations by introducing additional signal samples. Perhaps the most popular application of MSP is over-sampling analog-to-digital (A/D) and digital-to-analog (D/A) conversions. In over-sampling A/D, the signal is over-sampled thereby relaxing the anti-aliasing filter design requirements, and, hence, the hardware complexity.

In Chapter 2, we review basic signal processing concepts associated with audio coding. Chapter 3 provides introductory material to waveform quantization and entropy coding schemes. Some of the key topics covered in this chapter include scalar quantization, uniform/nonuniform quantization, pulse code modulation (PCM), differential PCM (DPCM), adaptive DPCM (ADPCM), vector quantization (VQ), bit-allocation techniques, and entropy coding schemes (Huffman, Rice, and arithmetic). Chapter 4 provides information on linear prediction and its application in narrow and wideband coding.

30. Tree-structured QMF bank. DISCRETE-TIME RANDOM SIGNALS 39 that the reconstructed signal in the z-domain has the form 1 1 ˆ X(z) = (H0 (z)F0 (z) + H1 (z)F1 (z))X(z) + (H0 (−z)F0 (z) 2 2 + H1 (−z)F1 (z))X(−z). 49) The signal X(−z) in Eq. 49) is associated with the aliasing term. The aliasing term can be cancelled by designing filters to have the following mirror symmetries: F1 (z) = −H0 (−z). 51) T (z) = (H0 (z)F0 (z) + H1 (z)F1 (z)). 2 If T (z) = 1, then the filter bank allows perfect reconstruction.

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