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Digital signal processing

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Digital signal processing (DSP) is the study of signals in a digital representation and the processing methods of these signals. DSP and analog signal processing are subsets of signal processing. It has three major subfields: audio signal processing, digital image processing and speech processing.

In DSP, engineers most commonly study digital signals in one of the following domains: time domain (one-dimensional signals), spatial domain (multidimensional signals), frequency domain, autocorrelation domain, and wavelet domains. They choose the domain in which to process a signal by making an educated guess (or trying out different possibilities) as to which domain best represents the essential characteristics of the signal. A sequence of samples from a measuring device produces a time or spatial domain representation, whereas a discrete Fourier transform produces the frequency domain information, that is the frequency spectrum. The autocorrelation is defined as the cross-correlation of the signal with itself over varying intervals of time or space.

Since the point of DSP is usually to measure or filter continuous real world analog signals, an analog to digital conversion performed by an analog to digital converter is usually the first step. The target of the signal processing is often another analog output signal which requires a digital to analog converter for translation.

The mathematical calculations and algorithms required for DSP are sometimes executed in hardware digital signal processors, also abbreviated DSP. Digital signal processors have heavily parallel architectures optimized for DSP computations and are designed to operate in real-time.

Signal sampling

A digital signal is often a numerical representation of a continuous analog signal (eg. a real world signal). This discrete representation of a continuous signal will generally introduce some error in to the data. The accuracy of the representation is mostly dependent on two things; sampling frequency and the number of bits used for the representation. The continuous signal is usually sampled at regular intervals by an Analog to digital converter and the value of the continuous signal in that interval is represented by a discrete value. The sampling frequency or sampling rate is then the rate at which new samples are taken from the continuous signal. The number of bits used for one value of the discrete signal tells us how accurately the signal magnitude is represented. Similarly, the sampling frequency controls the temporal or spatial accuracy of the discrete signal.

The Nyquist-Shannon sampling theorem, a fundamental theorem of signal processing, states that a sampled signal cannot unambiguously represent signal components with frequencies above half the sampling frequency. This frequency (half the sampling frequency) is called the Nyquist frequency. Frequencies above the Nyquist frequency N can be observed in the digital signal, but their frequency is ambiguous. That is, a frequency component with frequency f cannot be distinguished from another component with frequency 2N-f, 2N+f, 4N-f, etc. This is called aliasing. To handle this problem as gracefully as possible, most analog signals are filtered with an anti-aliasing filter (usually a low-pass filter) at the Nyquist frequency before conversion to the digital representation.


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Time and spatial domains

The most common processing approach in the time or spatial domain is enhancement of the input signal through a method called filtering. Filtering consists generally of some transformation of a number of surrounding samples around the current sample of the input and/or output signal. Properties such as the following characterize filters:

  • A "linear" filter consists of a linear transformation of input samples; other filters are "non-linear." Linear filters satisfy the superposition condition, i.e. if an input signal is a weighted linear combination of different input signals, the output will be an equally weighted linear combination of the corresponding individual output signals.
  • A "causal" transformation uses only previous samples of the input or output signals; transformations that also use future input samples are "non- causal." Adding a delay will transform many non-causal filters into causal filters.
  • A "time-invariant" filter has constant properties over time; other filters such as adaptive filters change in time.
  • "Finite impulse response" (FIR) filters use only the input signal; so-called "infinite impulse response" filters (IIR) use both the input signal and previous samples of the output signal. FIR filters are always stable. IIR filters may or may not be stable.

Most filters can, in Z-domain (frequency domain is a subset of Z-domain), be described by their Transfer functions. A filter may also be described as a difference equation, a collection of zeros and poles or, if it is an FIR filter, an impulse response or step response. The output of an FIR filter to any given input may be calculated by convolving the input signal with the impulse response.

Frequency domain

Signals are converted from time or spatial domain to the frequency domain usually through the Fourier transform. In Fourier transform the signal information is converted to a magnitude and phase component of each frequency. Regularly, the Fourier transform is converted to the power spectrum, which is the magnitude of each frequency component squared. The most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum to get information of which frequencies are present in the input signal and which are missing. However, there are some commonly used frequency domain transformations, for example, the cepstrum. In generation of the cepstrum, a signal is converted to the frequency domain through Fourier transform, then the logarithm is of the spectrum, which is converted back to time domain through the inverse Fourier transform. In the cepstrum, frequency components with smaller magnitude are thus emphasised while retaining the order of magnitudes of frequency components.

Applications

The main applications of DSP are audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition and digital communications. Specific examples are speech compression and transmission in (digital) mobile phones, equalisation of sound in Hifi-equipment, weather forecasting and economic forecasting, seismic data processing, analysis and control of industrial processes, computer-generated animations in movies and image manipulation.

Techniques:

Related fields:

References

  • Richard G. Lyons: Understanding Digital Signal Processing, Prentice Hall, ISBN 0131089897
  • Sen M. Kuo, Woon-Seng Gan: Digital Signal Processors: Architectures, Implementations, and Applications, Prentice Hall, ISBN 0130352144
  • Bernard Mulgrew, Peter Grant, John Thompson: Digital Signal Processing - Concepts and Applications, Palgrave Macmillan, ISBN 0-333-96356-3
  • Steven W. Smith: Digital Signal Processing - A Practical Guide for Engineers and Scientists, Newnes, ISBN 0-7506-7444-X
  • Paul A. Lynn, Wolfgang Fuerst: Introductory Digital Signal Processing with Computer Applications, John Wiley & Sons, ISBN 0-471-97984-8
  • James D. Broesch: Digital Signal Processing Demystified, Newnes, ISBN 1878707167
  • John Proakis, Dimitris Manolakis: Digital Signal Processing - Principles, Algorithms and Applications, Pearson, ISBN 0133942899
  • Hari Krishna Garg: Digital Signal Processing Algorithms, CRC Press, ISBN 0849371783
  • P. Gaydecki: Foundations Of Digital Signal Processing: Theory, Algorithms And Hardware Design, Institution of Electrical Engineers, ISBN 0852964315
  • Paul M. Embree, Damon Danieli: C++ Algorithms for Digital Signal Processing, Prentice Hall, ISBN 0131791443
  • Anthony Zaknich: Neural Networks for Intelligent Signal Processing, World Scientific Pub Co Inc, ISBN 9812383050
  • Vijay Madisetti, Douglas B. Williams: The Digital Signal Processing Handbook, CRC Press, ISBN 0849385725
  • Stergios Stergiopoulos: Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real-Time Systems, CRC Press, ISBN 0849336910
  • Joyce Van De Vegte: Fundamentals of Digital Signal Processing, Prentice Hall, ISBN 0130160776
  • Ashfaq Khan: Digital Signal Processing Fundamentals, Charles River Media, ISBN 1584502819
  • Jonathan M. Blackledge, Martin Turner: Digital Signal Processing: Mathematical and Computational Methods, Software Development and Applications, Horwood Publishing, ISBN 1898563489
  • Bimal Krishna, K. Y. Lin, Hari C. Krishna: Computational Number Theory & Digital Signal Processing, CRC Press, ISBN 0849371775
  • Doug Smith: Digital Signal Processing Technology: Essentials of the Communications Revolution, American Radio Relay League, ISBN 0872598195
  • Henrique S. Malvar: Signal Processing with Lapped Transforms, Artech House Publishers, ISBN 0890064679
  • Charles A. Schuler: Digital Signal Processing: A Hands-On Approach, McGraw-Hill, ISBN 0078297443
  • James H. McClellan, Ronald Schafer: Signal Processing First, Prentice Hall, ISBN 0130909998
  • Artur Krukowski, Izzet Kale: DSP System Design: Complexity Reduced Iir Filter Implementation for Practical Applications, Kluwer Academic Publishers, ISBN 1402075588
  • John G. Proakis: A Self-Study Guide for Digial Signal Processing, Prentice Hall, ISBN 0131432397
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