# Quantifying Spatial Audio Quality Impairment

arXiv: https://arxiv.org/abs/2306.08053 · HTML: https://mlsanity.com/paper/2306.08053/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2023.

## Built on by (most cited first, 3 of 3)

- [Learned Compression for Compressed Learning](https://mlsanity.com/paper/2412.09405/) (2024)
- [QASTAnet: A DNN-based Quality Metric for Spatial Audio](https://mlsanity.com/paper/2509.16715/) (2025)
- [LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation](https://mlsanity.com/paper/2605.06628/) (2026)

## Abstract (arXiv)

Spatial audio quality is a highly multifaceted concept, with many interactions between environmental, geometrical, anatomical, psychological, and contextual considerations. Methods for characterization or evaluation of the geometrical components of spatial audio quality, however, remain scarce, despite being perhaps the least subjective aspect of spatial audio quality to quantify. By considering interchannel time and level differences relative to a reference signal, it is possible to construct a signal model to isolate some of the spatial distortion. By using a combination of least-square optimization and heuristics, we propose a signal decomposition method to isolate the spatial error from a processed signal, in terms of interchannel gain leakages and changes in relative delays. This allows the computation of simple energy-ratio metrics, providing objective measures of spatial and non-spatial signal qualities, with minimal assumptions and no dataset dependency. Experiments demonstrate the robustness of the method against common spatial signal degradation introduced by, e.g., audio compression and music source separation. Implementation is available at https://github.com/karnwatcharasupat/spauq.