# Objective Measures of Perceptual Audio Quality Reviewed: An Evaluation of Their Application Domain Dependence

arXiv: https://arxiv.org/abs/2110.11438 · HTML: https://mlsanity.com/paper/2110.11438/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2021.

## Built on by (most cited first, 14 of 14)

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- [ConcateNet: Dialogue Separation Using Local And Global Feature Concatenation](https://mlsanity.com/paper/2408.08729/) (2024)
- [Assessing speech quality metrics for evaluation of neural audio codecs under clean speech conditions](https://mlsanity.com/paper/2509.24457/) (2025)
- [A Cold Diffusion Approach for Percussive Dereverberation](https://mlsanity.com/paper/2605.10256/) (2026)

## Abstract (arXiv)

Over the past few decades, computational methods have been developed to estimate perceptual audio quality. These methods, also referred to as objective quality measures, are usually developed and intended for a specific application domain. Because of their convenience, they are often used outside their original intended domain, even if it is unclear whether they provide reliable quality estimates in this case. This work studies the correlation of well-known state-of-the-art objective measures with human perceptual scores in two different domains: audio coding and source separation. The following objective measures are considered: fwSNRseg, dLLR, PESQ, PEAQ, POLQA, PEMO-Q, ViSQOLAudio, (SI-)BSSEval, PEASS, LKR-PI, 2f-model, and HAAQI. Additionally, a novel measure (SI-SA2f) is presented, based on the 2f-model and a BSSEval-based signal decomposition. We use perceptual scores from 7 listening tests about audio coding and 7 listening tests about source separation as ground-truth data for the correlation analysis. The results show that one method (2f-model) performs significantly better than the others on both domains and indicate that the dataset for training the method and a robust underlying auditory model are crucial factors towards a universal, domain-independent objective measure.