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arxiv: 2606.10010 · v1 · pith:36WYPCLPnew · submitted 2026-06-08 · 📡 eess.AS · cs.AI· cs.MM· cs.SD

DeRA-MOS: Optimizing Text-to-Music Evaluation via Decoupled Listwise Ranking and Modality Alignment

Chien-Chun Wang , Hung-Shin Lee , Hsin-Min Wang , Berlin Chen This is my paper

Pith reviewed 2026-06-27 14:48 UTC · model grok-4.3

classification 📡 eess.AS cs.AIcs.MMcs.SD
keywords text-to-music evaluationMOS estimationlistwise ranking lossmodality alignmentmusic impressiontext alignmentSpearman's rank correlation
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The pith

DeRA-MOS replaces point-wise training with listwise ranking and anchored alignment to better match human rank correlations in text-to-music evaluation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to fix automatic estimators of human mean opinion scores for text-to-music systems, which currently rely on point-wise regression that fails to optimize rank-based metrics and allows modality drift between audio and text. It introduces a decoupled framework where music impression uses a batch-aware listwise ranking loss to model relative orders and align with Spearman's correlation, while text alignment uses a score-anchored modality alignment loss to map scores to similarity targets and regularize the latent space. Experiments on MusicEval show gains in both ranking metrics. A reader would care because reliable automatic metrics would let developers test music generation models at scale without constant human listening tests. The central mechanism separates the two evaluation axes so each can receive a loss tailored to its evaluation criterion.

Core claim

By decoupling the optimization, the batch-aware listwise ranking loss for music impression models relative order within each mini-batch to align training directly with Spearman's rank correlation coefficient, while the score-anchored modality alignment loss for text alignment maps human scores to target audio-text similarity and regularizes the latent space before fusion; together these steps mitigate point-wise training mismatch and modality drift, producing substantial improvements in both MI and TA ranking metrics on the MusicEval dataset.

What carries the argument

Decoupled framework with batch-aware listwise ranking loss for relative order modeling in music impression and score-anchored modality alignment loss for regularizing audio-text latent space in text alignment.

If this is right

  • The listwise ranking loss directly targets relative ordering within batches to reduce mismatch with Spearman's coefficient for music impression.
  • The modality alignment loss regularizes the latent space by anchoring to human scores, reducing drift between audio and text representations.
  • The overall decoupled structure yields measurable gains on both MI and TA ranking metrics in experiments.
  • The approach supplies a training paradigm that can support evaluation of large numbers of text-to-music systems without proportional increases in human ratings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same separation of ranking and alignment objectives could be tested on other audio generation tasks where rank correlation is the primary evaluation measure.
  • If the batch listwise loss proves stable across different batch sizes, it might allow training on very large unlabeled music collections before fine-tuning on scored data.
  • Future work could replace the fixed anchor scores with learned targets to see whether further gains in cross-modal coherence are possible.

Load-bearing premise

The assumption that modeling relative orders inside each mini-batch with a listwise loss will improve alignment with global Spearman's rank correlation without creating batch-dependent artifacts.

What would settle it

Running the framework on a held-out music evaluation set and finding that the resulting SRCC scores for music impression or text alignment are no higher than those from standard point-wise regression baselines would falsify the improvement claim.

Figures

Figures reproduced from arXiv: 2606.10010 by Berlin Chen, Chien-Chun Wang, Hsin-Min Wang, Hung-Shin Lee.

Figure 1
Figure 1. Figure 1: Overview of the proposed DeRA-MOS framework. The archi￾tecture explicitly decouples the TTM evaluation process using task-specific objectives (dotted paths denote training-only operations). To overcome the limitations of homogeneous point-wise learning (LCE−Gauss), we introduce LBALR to enforce batch-aware global ranking for the MI branch. Crucially, for the TA branch, LSAMA is applied before cross-attenti… view at source ↗
Figure 2
Figure 2. Figure 2: Hyperparameter sensitivity analysis of DeRA-MOS on MusicEval. (a) [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
read the original abstract

Evaluating text-to-music (TTM) systems remains expensive because music impression (MI) and text alignment (TA) scores rely on human mean opinion scores (MOS). Most automatic MOS estimators are trained with point-wise regression or distributional classification. These objectives do not directly optimize rank-based metrics and provide weak geometric constraints for cross-modal coherence. To address these gaps, we propose DeRA-MOS, a decoupled optimization framework for TTM evaluation. For MI, we introduce a batch-aware listwise ranking loss that models relative order within each mini-batch and better aligns with evaluation based on Spearman's rank correlation coefficient (SRCC). For TA, we introduce a score-anchored modality alignment loss that maps human scores to target audio-text similarity and regularizes the latent space before fusion. By effectively mitigating the point-wise training mismatch and modality drift, experiments on MusicEval demonstrate that our decoupled framework yields substantial improvements in both MI and TA ranking metrics, establishing a robust paradigm for large-scale TTM evaluation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes DeRA-MOS, a decoupled optimization framework for automatic evaluation of text-to-music (TTM) systems. For music impression (MI), it introduces a batch-aware listwise ranking loss intended to better align training with Spearman's rank correlation coefficient (SRCC). For text alignment (TA), it introduces a score-anchored modality alignment loss to map human scores to audio-text similarity and regularize the latent space. The central claim is that this framework mitigates point-wise training mismatch and modality drift, yielding substantial improvements in MI and TA ranking metrics on the MusicEval dataset.

Significance. If the reported gains hold under rigorous evaluation, the work could advance automatic TTM evaluation by shifting from point-wise regression to objectives that more directly target rank-based metrics and cross-modal coherence. The decoupled design and explicit handling of batch-wise ranking are conceptually promising for large-scale evaluation pipelines.

major comments (2)
  1. [Abstract] Abstract: the claim of 'substantial improvements in both MI and TA ranking metrics' on MusicEval is presented without any numerical results, baseline comparisons, ablation studies, error bars, or dataset statistics. This absence makes the central empirical claim impossible to assess and is load-bearing for the paper's contribution.
  2. [Abstract] Abstract (MI loss paragraph): the batch-aware listwise ranking loss is asserted to model relative order within each mini-batch and thereby improve alignment with global SRCC. No description is given of loss aggregation across batches, temperature scaling, regularization, or cross-batch consistency mechanisms. Without these, per-batch permutations may not aggregate to a consistent global ranking on the full MusicEval test set, directly undermining the SRCC improvement claim.
minor comments (1)
  1. [Abstract] Abstract: the acronym 'DeRA-MOS' is used without expansion or definition.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We agree that the abstract requires strengthening to better support its central claims and will revise accordingly. Below we respond point-by-point to the major comments.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of 'substantial improvements in both MI and TA ranking metrics' on MusicEval is presented without any numerical results, baseline comparisons, ablation studies, error bars, or dataset statistics. This absence makes the central empirical claim impossible to assess and is load-bearing for the paper's contribution.

    Authors: We acknowledge that the abstract's empirical claim would be more assessable with supporting numbers. In the revised manuscript we will incorporate concise quantitative results (e.g., SRCC deltas versus baselines), a brief reference to the main baselines, and dataset size, while preserving abstract length. Full experimental details, ablations, and error bars remain in Sections 4 and 5. revision: yes

  2. Referee: [Abstract] Abstract (MI loss paragraph): the batch-aware listwise ranking loss is asserted to model relative order within each mini-batch and thereby improve alignment with global SRCC. No description is given of loss aggregation across batches, temperature scaling, regularization, or cross-batch consistency mechanisms. Without these, per-batch permutations may not aggregate to a consistent global ranking on the full MusicEval test set, directly undermining the SRCC improvement claim.

    Authors: The abstract is space-constrained and therefore high-level. The full manuscript (Section 3.2) specifies the listwise loss formulation, its temperature scaling, the per-batch application, and the epoch-wise aggregation that produces a consistent global ranking. We will add a short clause to the abstract mentioning temperature scaling and cross-batch consistency via stochastic optimization over the training set. This directly addresses the aggregation concern while the empirical SRCC results on the held-out test set serve as validation. revision: yes

Circularity Check

0 steps flagged

No circularity: new losses are independent of evaluation metrics

full rationale

The paper introduces two new training objectives (batch-aware listwise ranking loss for MI and score-anchored modality alignment loss for TA) that are optimized against human MOS labels on training data. Evaluation uses standard ranking metrics (SRCC, etc.) on the MusicEval test set. No quoted equations or self-citations show the claimed improvements reducing by construction to the inputs; the listwise loss models per-batch order but is not mathematically identical to global SRCC, and modality alignment is a separate regularizer. The derivation chain therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The listwise and alignment losses are new but their internal hyperparameters remain unspecified.

pith-pipeline@v0.9.1-grok · 5722 in / 1106 out tokens · 26782 ms · 2026-06-27T14:48:26.084814+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

37 extracted references · 2 canonical work pages · 2 internal anchors

  1. [1]

    AudioLDM: Text-to-audio generation with latent diffusion models,

    H. Liu, Z. Chen, Y . Yuan, X. Mei, X. Liu, D. Mandic, W. Wang, and M. D. Plumbley, “AudioLDM: Text-to-audio generation with latent diffusion models,” inProc. ICML, 2023, pp. 21 450–21 474

    2023

  2. [2]

    MusicLM: Generating Music From Text

    A. Agostinelli, T. I. Denk, Z. Borsos, J. Engel, M. Verzetti, A. Caillon, Q. Huang, A. Jansen, A. Roberts, M. Tagliasacchi, M. Sharifi, N. Zeghidour, and C. Frank, “MusicLM: Generating music from text,” 2023, arXiv:2301.11325. [Online]. Available: https://arxiv.org/abs/2301.11325

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  3. [3]

    Simple and controllable music generation,

    J. Copet, F. Kreuk, I. Gat, T. Remez, D. Kant, G. Synnaeve, Y . Adi, and A. Defossez, “Simple and controllable music generation,” inProc. NeurIPS, 2023, pp. 47 704–47 720

    2023

  4. [4]

    Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models,

    R. Huang, J. Huang, D. Yang, Y . Ren, L. Liu, M. Li, Z. Ye, J. Liu, X. Yin, and Z. Zhao, “Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models,” inProc. ICML, 2023, pp. 13 916– 13 932

    2023

  5. [5]

    MusicEval: A generative music dataset with expert ratings for automatic text-to-music evaluation,

    C. Liu, H. Wang, J. Zhao, S. Zhao, H. Bu, X. Xu, J. Zhou, H. Sun, and Y . Qin, “MusicEval: A generative music dataset with expert ratings for automatic text-to-music evaluation,” inProc. ICASSP, 2025, pp. 1–5

    2025

  6. [6]

    NISQA: A deep CNN- self-attention model for multidimensional speech quality prediction with crowdsourced datasets,

    G. Mittag, B. Naderi, A. Chehadi, and S. M ¨oller, “NISQA: A deep CNN- self-attention model for multidimensional speech quality prediction with crowdsourced datasets,” inProc. Interspeech, 2021, pp. 2127–2131

    2021

  7. [7]

    UTMOS: UTokyo-SaruLab system for V oiceMOS chal- lenge 2022,

    T. Saeki, D. Xin, W. Nakata, T. Koriyama, S. Takamichi, and H. Saruwatari, “UTMOS: UTokyo-SaruLab system for V oiceMOS chal- lenge 2022,” inProc. Interspeech, 2022, pp. 4521–4525

    2022

  8. [8]

    MOSNet: Deep learning-based objective assessment for voice conversion,

    C.-C. Lo, S.-W. Fu, W.-C. Huang, X. Wang, J. Yamagishi, Y . Tsao, and H.-M. Wang, “MOSNet: Deep learning-based objective assessment for voice conversion,” inProc. Interspeech, 2019, pp. 1541–1545

    2019

  9. [9]

    MBNET: MOS prediction for synthesized speech with mean-bias network,

    Y . Leng, X. Tan, S. Zhao, F. Soong, X.-Y . Li, and T. Qin, “MBNET: MOS prediction for synthesized speech with mean-bias network,” in Proc. ICASSP, 2021, pp. 391–395

    2021

  10. [10]

    Modelling inter-rater uncertainty in spoken language assessment,

    J. H. M. Wong, H. Zhang, and N. F. Chen, “Modelling inter-rater uncertainty in spoken language assessment,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 2886–2898, 2023

    2023

  11. [11]

    LDNet: Unified listener dependent modeling in MOS prediction for synthetic speech,

    W.-C. Huang, E. Cooper, J. Yamagishi, and T. Toda, “LDNet: Unified listener dependent modeling in MOS prediction for synthetic speech,” inProc. ICASSP, 2022, pp. 896–900

    2022

  12. [12]

    DeePMOS-B: Deep posterior mean-opinion-score Using beta distribution,

    X. Liang, F. Cumlin, V . Ungureanu, C. K. A. Reddy, C. Sch ¨uldt, and S. Chatterjee, “DeePMOS-B: Deep posterior mean-opinion-score Using beta distribution,” inProc. EUSIPCO, 2024, pp. 416–420

    2024

  13. [13]

    Rank consistent ordinal regres- sion for neural networks with application to age estimation,

    W. Cao, V . Mirjalili, and S. Raschka, “Rank consistent ordinal regres- sion for neural networks with application to age estimation,”Pattern Recognition Letters, vol. 140, pp. 325–331, 2020

    2020

  14. [14]

    ASTAR-NTU solution to AudioMOS challenge 2025 track1,

    F. Ritter-Gutierrez, Y .-C. Lin, J.-C. Wei, J. H. M. Wong, N. F. Chen, and H.-y. Lee, “ASTAR-NTU solution to AudioMOS challenge 2025 track1,” inProc. IEEE ASRU, 2025

    2025

  15. [15]

    Fr ´echet audio distance: A reference-free metric for evaluating music enhancement algorithms,

    K. Kilgour, M. Zuluaga, D. Roblek, and M. Sharifi, “Fr ´echet audio distance: A reference-free metric for evaluating music enhancement algorithms,” inProc. Interspeech, 2019, pp. 2350–2354

    2019

  16. [16]

    Adapting frechet audio distance for generative music evaluation,

    A. Gui, H. Gamper, S. Braun, and D. Emmanouilidou, “Adapting frechet audio distance for generative music evaluation,” inProc. ICASSP, 2024, pp. 1331–1335

    2024

  17. [17]

    Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation,

    Y . Wu, K. Chen, T. Zhang, Y . Hui, T. Berg-Kirkpatrick, and S. Dubnov, “Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation,” inProc. ICASSP, 2023, pp. 1–5

    2023

  18. [18]

    MuQ: Self-supervised music representation learning with mel residual vector quantization,

    H. Zhu, Y . Zhou, H. Chen, J. Yu, Z. Ma, R. Gu, Y . Luo, W. Tan, and X. Chen, “MuQ: Self-supervised music representation learning with mel residual vector quantization,”IEEE Transactions on Audio, Speech and Language Processing, vol. 33, pp. 3653–3664, 2025

    2025

  19. [19]

    Label distribution learning,

    X. Geng, “Label distribution learning,”IEEE Transactions on Knowl- edge and Data Engineering, vol. 28, no. 7, pp. 1734–1748, 2016

    2016

  20. [20]

    Soft labels for ordinal regression,

    R. Diaz and A. Marathe, “Soft labels for ordinal regression,” inProc. CVPR, 2019, pp. 4738–4747

    2019

  21. [21]

    Learning to rank: From pairwise approach to listwise approach,

    Z. Cao, T. Qin, T.-Y . Liu, M.-F. Tsai, and H. Li, “Learning to rank: From pairwise approach to listwise approach,” inProc. ICML, 2007, pp. 129–136

    2007

  22. [22]

    Listwise approach to learning to rank: Theory and algorithm,

    F. Xia, T.-Y . Liu, J. Wang, W. Zhang, and H. Li, “Listwise approach to learning to rank: Theory and algorithm,” inProc. ICML, 2008, pp. 1192–1199

    2008

  23. [23]

    Audioclip: Extending clip to image, text and audio,

    A. Guzhov, F. Raue, J. Hees, and A. Dengel, “Audioclip: Extending clip to image, text and audio,” inProc. ICASSP, 2022, pp. 976–980

    2022

  24. [24]

    Learning to rank with nonsmooth cost functions,

    C. Burges, R. Ragno, and Q. Le, “Learning to rank with nonsmooth cost functions,” inProc. NeurIPS, vol. 19, 2006

    2006

  25. [25]

    QAMRO: Quality-aware adaptive margin ranking optimiza- tion for human-aligned assessment of audio generation systems,

    C.-C. Wang, K.-T. Huang, C.-Y . Yang, H.-S. Lee, H.-M. Wang, and B. Chen, “QAMRO: Quality-aware adaptive margin ranking optimiza- tion for human-aligned assessment of audio generation systems,” in Proc. IEEE ASRU, 2025

    2025

  26. [26]

    A simple framework for contrastive learning of visual representations,

    T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” inProc. ICML, 2020, pp. 1597–1607

    2020

  27. [27]

    DRASP: A dual-resolution attentive statistics pooling frame- work for automatic MOS prediction,

    C.-Y . Yang, K.-T. Huang, C.-C. Wang, H.-S. Lee, H.-M. Wang, and B. Chen, “DRASP: A dual-resolution attentive statistics pooling frame- work for automatic MOS prediction,” inProc. APSIPA ASC, 2025, pp. 2038–2043

    2025

  28. [28]

    The AudioMOS challenge 2025,

    W.-C. Huang, H. Wang, C. Liu, Y .-C. Wu, A. Tjandra, W.-N. Hsu, E. Cooper, Y . Qin, and T. Toda, “The AudioMOS challenge 2025,” in Proc. IEEE ASRU, 2025

    2025

  29. [29]

    A New Measure of Rank Correlation,

    M. G. Kendall, “A New Measure of Rank Correlation,”Biometrika, 1938

    1938

  30. [30]

    Generalization ability of MOS prediction networks,

    E. Cooper, W.-C. Huang, T. Toda, and J. Yamagishi, “Generalization ability of MOS prediction networks,” inProc. ICASSP, 2022, pp. 8442– 8446

    2022

  31. [31]

    RoBERTa: A Robustly Optimized BERT Pretraining Approach

    Y . Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V . Stoyanov, “RoBERTa: A robustly optimized BERT pretraining approach,” 2019, arXiv:1907.11692. [Online]. Available: http://arxiv.org/abs/1907.11692

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  32. [32]

    Decoupled weight decay regularization,

    I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” inProc. ICLR, 2019

    2019

  33. [33]

    Wilcoxon signed-rank test,

    R. F. Woolson, “Wilcoxon signed-rank test,” inWiley Encyclopedia of Clinical Trials, 2008, pp. 1–3

    2008

  34. [34]

    Gradient surgery for multi-task learning,

    T. Yu, S. Kumar, A. Gupta, S. Levine, K. Hausman, and C. Finn, “Gradient surgery for multi-task learning,” inProc. NeurIPS, vol. 33, 2020, pp. 5824–5836

    2020

  35. [35]

    Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,

    J. Kim, J. Kong, and J. Son, “Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,” inProc. ICML, 2021, pp. 5530–5540

    2021

  36. [36]

    HiFi-GAN: Generative adversarial net- works for efficient and high fidelity speech synthesis,

    J. Kong, J. Kim, and J. Bae, “HiFi-GAN: Generative adversarial net- works for efficient and high fidelity speech synthesis,” inProc. NeurIPS, vol. 33, 2020, pp. 17 022–17 033

    2020

  37. [37]

    Maximum likelihood estimation of observer error-rates using the EM algorithm,

    A. P. Dawid and A. M. Skene, “Maximum likelihood estimation of observer error-rates using the EM algorithm,”Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 28, no. 1, pp. 20– 28, 1979

    1979