ABSTRACT
In this chapter, we review properties of auditory perception that are relevant to the design of coders for acoustic signals. The chapter begins with a general definition of a perceptual coder, then considers what the ‘‘ideal’’ psychophysical model would consist of, and what use a coder could be expected to make of this model. We then present some basic definitions and concepts. The chapter continues with a review of relevant psychophysical data, including results on threshold, just-noticeable differences (JNDs), masking, and loudness. Finally, we attempt to summarize the present state of the art, the capabilities and limitations of present-day perceptual coders for audio and speech, and what areas most need work.
A coded signal differs in some respect from the original signal. One task in designing a coder is to minimize some measure of this difference under the constraints imposed by bit rate, complexity, or cost. What is the appropriate measure of difference? The most straightforward approach is to minimize some physical measure of the difference between original and coded signal. The designer might attempt to minimize RMS difference between the original and coded waveform, or perhaps the difference between original and coded power spectra on a frame-by-frame basis. However, if the purpose of the coder is to encode acoustic signals that are eventually to be listened to* by people, these physical measures do not directly address the appropriate issue. For signals that are to be listened to by people, the ‘‘best’’ coder is the one that sounds the best. There is a very clear distinction between ‘‘physical’’ and ‘‘perceptual’’ measures of a signal (frequency vs. pitch, intensity vs. loudness, for example). A perceptual coder can be defined as a coder that minimizes some measure of the difference between original and coded signal so as to minimize the perceptual impact of the coding noise. We can define the best coder given a particular set of constraints as the one in which the coding noise is least objectionable.
