Exploring Perceptual Audio Quality Measurement on Stereo Processing Using the Open Dataset of Audio Quality

TL;DR

This paper evaluates how well current objective audio quality metrics predict perceived quality in stereo processing, using the ODAQ dataset that includes subjective ratings for various distortions and spatial processing methods.

Contribution

It provides an in-depth analysis of the performance of existing audio quality metrics on stereo processing tasks, highlighting the need to incorporate psychoacoustic and contextual factors.

Findings

Timbre-focused metrics perform well in simple conditions.

Prediction accuracy decreases with complex stereo processing.

Modeling psychoacoustic and contextual interactions is crucial for better quality assessment.

Abstract

ODAQ (Open Dataset of Audio Quality) provides a comprehensive framework for exploring both monaural and binaural audio quality degradations across a range of distortion classes and signals, accompanied by subjective quality ratings. A recent update of ODAQ, focusing on the impact of stereo processing methods such as Mid/Side (MS) and Left/Right (LR), provides test signals and subjective ratings for the in-depth investigation of state-of-the-art objective audio quality metrics. Our evaluation results suggest that, while timbre-focused metrics often yield robust results under simpler conditions, their prediction performance tends to suffer under the conditions with a more complex presentation context. Our findings underscore the importance of modeling the interplay of bottom-up psychoacoustic processes and top-down contextual factors, guiding future research toward models that more effectively integrate both timbral and spatial dimensions of perceived audio quality.