Computational model for predicting sound quality metrics using loudness model based on gammatone/gammachirp auditory filterbank and its applications

Abstract

Sound quality metrics (SQMs), such as sharpness, roughness, and fluctuation strength, have been utilized to study the perceived pleasantness or unpleasantness of sounds. Computational models for predicting SQMs have been proposed, commonly based on Zwicker's loudness method (ISO 532B:1975). ISO 532 was revised in 2017; Moore & Glasberg's loudness method for stationary sound was adopted in ISO 532-2:2017, while Zwicker's loudness method for stationary and time-varying sound was revised in ISO 532-1:2017. There are a few important differenterences between the two methods, such as the frequency scale (Bark scale or ERB scale) and filter shape (symmetry or asymmetry). It therefore might be necessary to revise these computational models for predicting SQMs on the basis of these revisions. We previously proposed a time-domain computational model for predicting SQMs on the basis of a loudness method using the time-domain auditory filterbank. This loudness method can be regarded as a time-domain version of Moore & Glasberg's method in ISO 532-2:2017, using gammatone/gammachirp auditory filterbanks instead of the roex auditory filterbank, which does not have any time-domain representations. In this paper, we introduce calculations for three SQMs (sharpness, roughness, and fluctuation strength) using the proposed model for predicting SQMs.