DIGITAL SIGNAL PROCESSING – An Introduction Dr.Y. Narasimha Murthy Ph.D Reader, Department of Physics & Electronics SRI SAI BABA NATIONAL COLLEGE (Autonomous) ANANTAPUR – 515001 (A.P) yayavaram@yahoo.com

Organisation:

Organisation What is signal processing Need of Processing Catagories Advantages of Digital over analog Filters-Analog & Digital Comparison Digital filters-Types

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FIR &IIR Advantages-Disadvantages Applications of DSP Simple Illustrations Various types of DSPs Microprocessor & Signal processor Architecture Books &Web resources

What is Signal processing?:

What is Signal processing? Signal processing is the analysis, interpretation, and manipulation of signals like sound, images time-varying measurement values and sensor data etc… For example biological data such as electrocardiograms, control system signals, telecommunication transmission signals such as radio signals, and many others.

Need of Signal Processing:

Need of Signal Processing When a signal is transmitted from one point to another there is every possibility of contamination /deformation of the signal by external noise. So to retrieve the original signal at the receiver suitable filters are to be used. i.e the signal is processed to obtain the pure signal.

Categories of signal processing:

Categories of signal processing Analog signal processing — for signals that have not been digitized, as in classical radio, telephone, radar, and television systems. This involves linear electronic circuits such as passive filters , active filters , additive mixers , integrators and delay lines .

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It also involves non-linear circuits such as compandors , multiplicators ( frequency mixers and voltage-controlled amplifiers ), voltage-controlled filters , voltage-controlled oscillators and phase-locked loops . Digital signal processing — for signals that have been digitized, processing is done by general-purpose computers or by digital circuits such as ASICs , field-programmable gate arrays or specialized digital signal processors (DSP chips).

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So the processing of the signal helps to estimate characteristic parameters of the signal and also to transform the signal in to the desired form.

Analog signal processing:

Analog signal processing The analog signal processing is basically, filtering of the signal . It can be denoted by the following diagram.

Digital signal processing-Block diagram:

Digital signal processing-Block diagram The digital signal processor consists of anti-aliasing filter, analog to digital converter (ADC), a digital filter represented by the transfer function H(z), a digital to analog converter and a reconstruction filter.

Advantages of Digital over analog signal processing:

Advantages of Digital over analog signal processing Accuracy: The analog circuits are prone to temperature and external effects, but the digital filters have no such problems. Flexibility: Reconfiguration of analog filters is very complex whereas the digital filters can be reconfigured easily by changing the program coefficients .

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Digital signals can be easily stored on any magnetic media or optical media are using semiconductor chips. Easy operation: Even complex mathematical operations can be performed easily using computers, which is not the case with analog processing.

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Multiplexing: Digital signal processing provides the way for Integrated service digital network (ISDN) where digitized signals can be multiplexed with other digital data and transmitted through the same channel.

Limitations:

Limitations There are also certain limitations in DSP. Bandwidth restrictions Speed limitations Finite word length problems.

Filters:

Filters Any one who observes the DSP block diagram finds that the filter is the main component of DSP. Filters have two uses Signal separation Signal restoration

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Signal separation is needed when a signal has been contaminated with interference, noise or other signals. Signal restoration is used when a signal has been distorted in some way or other. For example an audio recording made with poor equipment may be filtered to get the original sound.

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Another example of deblurring of an image occurred with an improperly focused lens or a shaky camera. So these problems can be solved with either analog or digital filters.

Analog filters:

Analog filters Analog filters take the analog signal as input and process the signal and finally gives the analog output. An analog filter is constructed using resistors, capacitors, active components etc…

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A simple analog low pass filter is shown below

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Coming to advantages of Analog filters they are cheap and have a large dynamic range in both amplitude and frequency. But in terms of performance they are not superior to digital filters.

Digital filters:

Digital filters A digital filter processes and generates digital data. A digital filter constitutes elements like adder, multiplier and delay units. Digital filters are vastly superior in the level of performance in comparison to analog filters.

A simple digital filter:

A simple digital filter

Advantages ::

Advantages : There are many advantages with digital filters. Unlike analog filters ,the digital filter performance is not influenced by component ageing, temperature and power variations .

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A digital filter is highly immune to noise and relatively stable. Digital filters afford a wide variety of shapes for the amplitude and phase responses. Impedance matching problems are minimum.

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Transportation and reconfiguration is very easy ,which is not true in the case of analog filters. Multiple filtering is possible only in digital filters. Computational problems are minimum.

Disadvantages::

Disadvantages: There are few disadvantages also. Quantization error occurs due to finite word length in the representation of signals and parameters. Digital filters also suffer from Bandwidth problems.

Differences between analog and digital filters ::

Differences between analog and digital filters : An analog filter is constructed using active, passive components like resistors, capacitors and op amps etc.. A digital filter constitutes adder, multiplier and delay elements An analog filter is denoted by a differential equation. A digital filter is denoted by a difference equation.

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Laplace transform is used for the analysis of analog filter. Z transforms are used for the analysis of digital filters. The frequency response of an analog filter can be modified by changing the components. The frequency response can be changed by changing the filter coefficients.

Types of Digital filters:

Types of Digital filters Broadly speaking ,two types of digital filters exists. FIR Filters(Finite impulse response filters) IIR Filters (Infinite Impulse response filters)

FIR Filters:

FIR Filters The digital filter whose impulse response is of finite duration is known as Finite impulse response filter. The response of the FIR filter depends only on the present and past input samples. These FIR filters are also called non recursive filters. So, in FIR the impulse response sequence is of finite duration, i.e. it has a finite number of non-zero terms.

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The system with the impulse response denotes an FIR system.

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Ex: The following difference equation denotes the finite impulse response filter. For i<0, y(n)=0 i.e. the impulse response is finite and it exists only for n>0

Advantages of FIR Filters:

Advantages of FIR Filters FIR filters can be designed with exact linear phase. These linear phase filters are important for applications where frequency dispersion due to non-linear phase is hazardous. (For example speech processing and data transmission) FIR filters are stable Round off noise can be eliminated in FIR filters

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FIR filters can be efficiently implemented in multirate DSP systems FIR filters reduce the computation complexity

Disadvantages:

Disadvantages As large number of impulse response samples are required to properly approximate sharp cutoff FIR filters the processing will become complex due to slow convolution. The delay of linear phase FIR filters can sometimes create problems in some DSP applications .

IIR Filters:

IIR Filters The digital filter whose impulse response is of infinite duration is known as Infinite impulse response filter. The response of an IIR filter is a function of current and past input signal samples and past output signal samples. It is also called recursive filter.

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Ex: A simple first order difference equation illustrates the IIR filter. The implementation diagram of first order IIR filter is shown in the next slide.

Advantages of IIR filters:

Advantages of IIR filters An IIR filter has lesser number of side lobes in the stop-band than an FIR filter with the same number of parameters. Also the implementation of an IIR filter involves fewer parameters, less memory requirements and lower computational complexity.

Disadvantages:

Disadvantages IIR filters do not have linear phase and also they are not very stable. Realization of IIR filters is not very easy as compared to FIR filters As it is a recursive filter the number of coefficients is very large and the memory requirements are also high

Applications of DSP:

Applications of DSP Digital signal processing has variety of applications in diverse fields like Digital filtering Spectral analysis Speech processing Image processing Radar and sonar processing

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Disk and robot control Telecommunication Consumer electronics Biomedical engineering Military applications

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Let us discuss few examples of DSP in musical sound processing. In all musical recordings, the sound from instruments is recorded in studio and then special audio effects are added by manipulating the recorded musical sounds. The audio effects are artificially generated using various DSP techniques.

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The sound reaching the listeners in a concert hall during a musical programme consists of direct sound, early reflections and reverberations (echoes). But the sound recorded in a studio is different and it doesn’t sound natural.

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So, echoes are simply generated by delay units. The direct sound and a single echo appear in K sampling period latter can be generated by the FIR filter with the system function The realization of the echo filter is shown in the next slide.

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To generate multiple echoes separated K sapling periods we can use an FIR filter with transfer function

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Similarly an infinite number of echoes spaced K sampling periods apart with exponentially decaying amplitudes can be created by an IIR filter. The realization of infinite echo generator is shown in the next slide.

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The other special sound effects are flanging and chorus. The flanging effect is created by feeding the same musical note to two tape recorders and then combining their delayed outputs. This effect can be simulated using the FIR filter by periodically varying the delay K(n) between 0 and K.

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The functional diagram of flanging effect generator is shown in the next slide.

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The chorus effect is achieved when several musicians are playing the same musical note at the same time with small changes in the amplitudes and small timing differences between their sounds.

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A chorus generator can be realized by parallelly connecting few number of flanging effect filters.

Various DSP Processors:

Various DSP Processors Texas DSP Processors 16-bit Fixed point arithmetic processors TMS320C1X TMS320C2X TMS320C5X TMS320C8X 32-bit floating point arithmetic processors TMS320C3X TMS320C4X

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Analog devices DSP Processors Blackfin SHARC TigerSHARC ADSP-21XX – 16 bit fixed point processor ADSP-210XX- 32 bit floating point processor

Differences between microprocessor and Digital signal processor:

Differences between microprocessor and Digital signal processor A microprocessor with its limited speed is meant for low speed applications whereas the DSP is meant for fast real time applications. Generally microprocessors use Van-nuemann architecture whereas most of the DSP processors use a modified Harvard architecture with two or three memory buses.

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OR Bus General purpose processors Early DSP processors More optimized DSP processors

Simple architecture of DSP processor:

Simple architecture of DSP processor

The books which have helped me to understand DSP:

The books which have helped me to understand DSP 1. Oppenheim, A.V. and Schefar, Digital signal processing, PHI 2. Oppenheim, Applications of digital signal processing, PHI 3. Rabir and Gold, Theory of digital signal processing, PHI 4. Proakis and Manolakis, Digital signal processing, Pearson publishers

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5. Antoniou, A. Digital filters analysis, design applications, McGraw Hill 6. Johnson, J.R. Introduction to digital signal processing , PHI 7. Vanvalkenburg.M.V. Analog filter design, Sanders publishers 8. Vinay K. Ingle, Proakis, Digital signal processing using MATLAB, Bookware series

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9. Sajeeth K. Mithra, Digital signal processing, TMH 10. V.K. Khanna, Digital signal processing, telecommunication, multimedia technology, Wheeler publishers 11. P. Rameshbabu, Digital signal processing, Scitech publishers. 12. Salivana, Digital signal processing, TMH

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13. Ifeachor and Jervis, Digital signal processing-a practical approach, Addison Wesley publishers 14. Sarkar N. Elements of digital signal processing, Kanna publishers 15. Defetta D.J. Digital signal processing, John Wieley publishers 16. Lyons R.G. Understanding digital signal processing,Addition Wesley

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17. B. Venkataramani, Digital signal processors, TMH 18. Chapman J. Stephen, MATLAB Programming for engineers, Bookware Series 19. Ramachandran.B. Digital signal processing, Anuradha publishers 20. Bose N.K. Digital filters-Theory and applications, Elsevier publishers 21. The Scientist and Engineer's and Guide to Digital Signal Processing by Steven W. Smith.(On line text)

Web References:

Web References www.ti.com www.analog.com www.dspguru.com www.mathworks.com www.dsptutor.freeuk.com www.dspguide.com www.elsevier.com/locate/dsp (On line journal) dsp.rice.edu (rice university) www.youtube.com (lecture on DSP )

Concluding Remarks:

Concluding Remarks The woods are lovely, dark and deep, But I have promises to keep, And miles to go before I sleep, And miles to go before I sleep. ---- Robert Frost GOOD LUCK!

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