Download Digital Speech, 2nd Edition: Coding for Low Bit Rate by A. M. Kondoz PDF
By A. M. Kondoz
Development at the good fortune of the 1st variation electronic Speech bargains huge new, up-to-date and revised fabric dependent upon the newest examine. This moment version keeps to supply the elemental technical heritage required for low bit price speech coding and the most well liked advancements in electronic speech coding ideas which are acceptable to evolving verbal exchange structures. Written by means of a widely known, hugely revered educational, this authoritative quantity should be worthy to training engineers, community designers, computing device scientists and complicated scholars in communications, electric and digital engineering.
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Construction at the good fortune of the 1st variation electronic Speech bargains vast new, up-to-date and revised fabric established upon the most recent study. This moment variation keeps to supply the elemental technical history required for low bit price speech coding and the most well liked advancements in electronic speech coding strategies which are acceptable to evolving conversation platforms.
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Extra resources for Digital Speech, 2nd Edition: Coding for Low Bit Rate Communication Systems
Better performance can be achieved with a closed loop scheme where the shape is first found and then the corresponding gain is quantized before computing the final error. Here, we assume an optimum gain σk to be used for each of the L shape codebook entries and compute the corresponding distortion Dk as: N (xi − σk yki )2 Dk = k = 1, 2, . . 65) i=1 We wish to find a vector yk from the shape codebook with a gain value of σk such that the corresponding distortion Dk is minimized. However, we have two unknowns, namely, yk and σk .
Therefore, it is extremely important to train these codebooks for optimal performance with varying time and hence varying input vector characteristics. One way of making a codebook track the input vector characteristics with time is to make the codebook adaptive. As in the case of an adaptive scalar quantizer, the adaptation of a codebook can be achieved using either forward or backward schemes. In a forward adaptive vector quantizer, the codebook is updated with respect to the input vectors before the quantization process, which requires some side information to be transmitted to the de-quantizer for compatible adaptation necessary for correct recovery of the signal.
In order to avoid aliasing distortion, either the input analogue signal has to be band-limited to a maximum of half the sampling frequency or the sampling frequency has to be increased to at least twice the highest frequency in the analogue signal. 6) n=−∞ where φ = π(t − nT)/T. Therefore, if the sampling frequency is at least twice the Nyquist frequency, the analogue signal can be recovered completely from its sampled version by adding together sinc functions centred on each sampling point and scaled by the sampled value of the analogue signal.