Abstract
The Spatial Audio Quality Inventory (SAQI, Lindau et al. 2014 [1]) defines a comprehensive list of attributes for quality assessment of spatial audio. These attributes are traditionally used in perceptual experiments. However, automatic evaluation is a common alternative to assess spatial audio algorithms by means of acoustic recordings and numerical methods. This study aims at bridging the gap between perceptual evaluation and automatic assessment methods. We performed a focused literature review on available auditory models and proposed a list to cover the attributes in SAQI based on self-imposed selection criteria , such as binaural compatibility. The selected models are publicly available and ready to be used in automatic assessment methods. This Spatial Audio Models' Inventory (SAMI) could serve as relevant metrics to train and/or optimise machine-learning and deep-learning algorithms when the objective is to improve the perceived quality of reproduction in spatial audio applications. Moreover, SAMI composes a benchmark to challenge novel models.