Download Discrete-Time Signal Processing (2nd Edition) (Prentice-Hall by Alan V. Oppenheim, Ronald W. Schafer, John R. Buck PDF

By Alan V. Oppenheim, Ronald W. Schafer, John R. Buck

For senior/graduate-level classes in Discrete-Time sign Processing. THE definitive, authoritative textual content on DSP - excellent for people with an introductory-level wisdom of indications and structures. Written by means of well known, DSP pioneers, it offers thorough therapy of the elemental theorems and homes of discrete-time linear platforms, filtering, sampling, and discrete-time Fourier research. via targeting the overall and common options in discrete-time sign processing, it is still important and suitable to the hot demanding situations coming up within the box -without proscribing itself to precise applied sciences with rather brief lifestyles spans.

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Additional resources for Discrete-Time Signal Processing (2nd Edition) (Prentice-Hall Signal Processing Series)

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1 Discrete-Time Signals: Sequences 11 of samples of the speech signal. Although the original speech signal is defined at all values of time t, the sequence contains information about the signal only at discrete instants. From the sampling theorem, discussed in Chapter 4, the original signal can be reconstructed as accurately as desired from a corresponding sequence of samples if the samples are taken frequently enough. 1 Basic Sequences and Sequence Operations In the analysis of discrete-time signal-processing systems, sequences are manipulated in several basic ways.

From Eq. 110), we see that H( eiw) describes the change in complex amplitude of a complex exponential input signal as a function of the frequency w. The eigenvalue H(eiw) is called the frequency response of the system. 111) Sec. 6 Frequency-Domain Representation of Discrete-Time Signals and Systems 41 or in terms of magnitude and phase as H(eiw) Example 2. '17 System = I H(ei"')lei

According to the commutative property of convolution, the order in which systems are cascaded does not matter, as long as they are linear and time invariant. 13b ). Also, it follows from Eq. each cascade system is the convolution of the individual impulse responses. 79) = o[n]- 8[n -1]. 13(c). 13(a) and (b) have been converted to causal systems by cascading them with a delay. In general, any noncausal FIR system can be made causal by cascading it with a sufficiently long delay. Another example of cascaded systems introduces the concept of an inverse system.

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