# QASTAnet: A DNN-based Quality Metric for Spatial Audio

arXiv: https://arxiv.org/abs/2509.16715 · HTML: https://mlsanity.com/paper/2509.16715/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 6 of 6)

- [Adaptive pooling operators for weakly labeled sound event detection](https://mlsanity.com/paper/1804.10070/) (2018)
- [Quantifying Spatial Audio Quality Impairment](https://mlsanity.com/paper/2306.08053/) (2023)
- [Generative Machine Listener](https://mlsanity.com/paper/2308.09493/) (2023)
- [Perceptually-motivated Spatial Audio Codec for Higher-Order Ambisonics Compression](https://mlsanity.com/paper/2401.13401/) (2024)
- [Objective Assessment of Spatial Audio Quality using Directional Loudness Maps](https://mlsanity.com/paper/2212.01451/) (2022)
- [Loss functions incorporating auditory spatial perception in deep learning -- a review](https://mlsanity.com/paper/2506.19404/) (2025)

## Abstract (arXiv)

In the development of spatial audio technologies, reliable and shared methods for evaluating audio quality are essential. Listening tests are currently the standard but remain costly in terms of time and resources. Several models predicting subjective scores have been proposed, but they do not generalize well to real-world signals. In this paper, we propose QASTAnet (Quality Assessment for SpaTial Audio network), a new metric based on a deep neural network, specialized on spatial audio (ambisonics and binaural). As training data is scarce, we aim for the model to be trainable with a small amount of data. To do so, we propose to rely on expert modeling of the low-level auditory system and use a neurnal network to model the high-level cognitive function of the quality judgement. We compare its performance to two reference metrics on a wide range of content types (speech, music, ambiance, anechoic, reverberated) and focusing on codec artifacts. Results demonstrate that QASTAnet overcomes the aforementioned limitations of the existing methods. The strong correlation between the proposed metric prediction and subjective scores makes it a good candidate for comparing codecs in their development.