A Low-Complexity Spectro-Temporal Distortion Measure for Audio Processing Applications

摘要

Perceptual models exploiting auditory masking are frequently used in audio and speech processing applications like coding and watermarking. In most cases, these models only take into account spectral masking in short-time frames. As a consequence, undesired audible artifacts in the temporal domain may be introduced (e.g., pre-echoes). In this article we present a new low-complexity spectro-temporal distortion measure. The model facilitates the computation of analytic expressions for masking thresholds, while advanced spectro-temporal models typically need computationally demanding adaptive procedures to find an estimate of these masking thresholds. We show that the proposed method gives similar masking predictions as an advanced spectro-temporal model with only a fraction of its computational power. The proposed method is also compared with a spectral-only model by means of a listening test. From this test it can be concluded that for non-stationary frames the spectral model underestimates the audibility of introduced errors and therefore overestimates the masking curve. As a consequence, the system of interest incorrectly assumes that errors are masked in a particular frame, which leads to audible artifacts. This is not the case with the proposed method which correctly detects the errors made in the temporal structure of the signal.