The world is full of signals: images from digital cameras, voltages generated by the heart, seismic vibrations, audio being transmitted over radio waves, radar and sonar signals, sound pressure waves from speech and countless others are all part of our daily interaction with our surrounding environment. Signal processing is the science of analysing, synthesizing, interpreting and manipulating signals to develop an understanding of the data and is generally sub-divided into analogue signal processing and digital signal processing.

Analogue signals vary continuously according to the information while digital signals represent the information as a series of discrete values. With the advent of the computer age, digital signal processing in particular has become a powerful tool which has found its home in many applications. Analogue signals are typically converted into digital signals through the use of analogue-to-digital converters to exploit the potential of computers for the processing of information.

Numerous methods are employed for the processing of signals, many of which derive from mathematical transforms (e.g. Fourier transform), statistics (e.g. Gaussian mixture models) and estimation theory. The development of algorithms and efficient implementations are a core component of signal processing and rely on operations such as filtering, coding, analysis and synthesis to achieve an understanding of the data being manipulated.

The applications for signal processing are plentiful and span a diverse range of fields including, but not limited to: audio and speech processing, image processing, data compression, telecommunications, medical imaging, computer generated animation, seismic data processing and financial forecasting.

Reference

Alan V. Oppenheim, Ronald W. Schafer, John R. Buck: Discrete-Time Signal Processing, Prentice Hall, 1989.

Summary Written By

David Gunawan

School of Electrical Engineering & Telecommunications

University of New South Wales