Fundamentals of Noise Reduction

Abstract

The existence of noise is inevitable. In all applications that are related to voice and speech, from sound recording, telecommunications, and telecollaborations, to human-machine interfaces, the signal of interest that is picked up by a microphone is generally contaminated by noise. As a result, the microphone signal has to be cleaned up with digital signal-processing tools before it is stored, analyzed, transmitted, or played out. The cleaning process, which is often referred to as either noise reduction or speech enhancement, has attracted a considerable amount of research and engineering attention for several decades. Remarkable advances have already been made, and this area is continuing to progress, with the aim of creating processors that can extract the desired speech signal as if there is no noise. This chapter presents a methodical overview of the state of the art of noise-reduction algorithms. Based on their theoretical origin, the algorithms are categorized into three fundamental classes: filtering techniques, spectral restoration, and model-based methods. We outline the basic ideas underlying these approaches, discuss their characteristics, explain their intrinsic relationships, and review their advantages and disadvantages.