Design Choices in a Binaural Perceptual Model for Improved Objective Spatial Audio Quality Assessment

Spatial audio quality assessment is crucial for attaining immersive user experiences, but subjective evaluations are time-consuming and costly. Thus, automated algorithms have been developed for objective quality assessment. This study focuses on the development of an improved binaural perceptual model for spatial audio quality measurement by choosing the best-performing set of design parameters among previously proposed methods. Existing binaural models, particularly extensions of the Perceptual Evaluation of Audio Quality (PEAQ) method, are investigated to enhance spatial audio quality metrics. The performance of the popular Gammatone Filter Bank (GTFB) and PEAQ’s built-in filter bank is compared for its use in constructing spatial distortion metrics related to three binaural cues: inter-aural time and level differences (ITD and ILD) and inter-aural cross-correlation (IACC). Evaluation includes different binaural cue types and window lengths, with subjective scores from a spatial audio quality database used for correlation analysis. Additionally, three binaural cue extraction systems are evaluated using spatial and timbre distortion metrics, employing a common peripheral model. Objective quality scores are derived using multivariate regression and validated against subjective scores from multiple listening test databases. Results indicate similar performance between GTFB and PEAQ’s filter bank in predicting spatial audio quality, making an additional GTFB unnecessary for spatial audio quality assessment. The binaural cue extraction model proposed by Seo et al. (2013) demonstrates the best overall performance. These results can the inform design choices made in developing a binaural model that incorporates higher-level spatial distortion metrics, such as directional loudness. Accurate spatial audio quality metrics can improve the design of spatial processing algorithms for an enhanced immersive user experience.