Noise Reduction In Digital Signal Processing

Abstract:

With the increasing use of digital cellular mobile systems in a variety of adverse environments, noise reduction is becoming a particularly important aspect of communication system design.

Noise can be defined as an unwanted signal that interferes with the communication or measurement of another signal. The various types of noise present in digital systems are white noise, coloured noise, impulsive noise, transient noise pulses, thermal noise etc.

Signal distortion is the term often used to describe a systematic undesirable change in a signal and refers to changes in a signal due to the non-ideal characteristics of the transmission channel, reverberations, echo and missing samples. Noise and distortion are the main limiting factors in communication and measurement systems. Therefore the modelling and removal of the effects of noise and distortion have been at the core of the theory and practice of communications and signal processing. Noise reduction and distortion removal are important problems in applications such as cellular mobile communication, speech recognition, image processing, medical signal processing, radar, sonar, and in any application where the signals cannot be isolated from noise and distortion.

Various types of filters are used for reduction of noise. Some of them are Wiener filters, Adaptive filters.

Wiener filters play a central role in wide range of applications such as linear prediction, echo cancellation, signal restoration, channel equalization and system identification. Adaptive filters are used for non-stationary signals and environments or in applications where a sample by sample adaptation of a process or a low processing delay is required.

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