pith. sign in

arxiv: 2605.17512 · v1 · pith:T353RQIGnew · submitted 2026-05-17 · 📡 eess.AS · cs.SD

Robust Audio Tagging under Class-wise Supervision Unreliability

Yuanbo Hou , Zhaoyi Liu , Tong Ye , Qiaoqiao Ren , Jian Guan , Wenwu Wang , Stephen Roberts This is my paper

Pith reviewed 2026-05-19 22:22 UTC · model grok-4.3

classification 📡 eess.AS cs.SD
keywords audio taggingweak supervisionlabel noiseunreliable labelsrobust trainingAudioSetclass-wise modelinggenerated audio
0
0 comments X

The pith

Learning one unreliability scalar per sound class down-weights noisy labels and improves audio tagging on weak data.

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

The paper establishes that supervision errors in large audio datasets vary by class and can be corrected by learning a dedicated scalar weight for each class. This weight reduces the influence of unreliable labels from spurious additions, class confusions, and weak evidence during training. The method requires no architecture changes and leaves inference unchanged. A reader would care because datasets like AudioSet mix real and generated audio with inconsistent annotation quality, and class-specific correction reduces the resulting optimization bias.

Core claim

Explicit class-wise modeling of supervision unreliability is an effective and practical strategy for robust audio tagging under large-scale weakly labeled training. The approach learns a separate unreliability parameter for each class and uses it to control supervision strength, addressing spurious label additions, misassignments between similar classes, and weakened label evidence without modifying the model or inference process.

What carries the argument

The Class-wise Supervision Unreliability (CSU) framework, which optimizes one scalar unreliability parameter per class to down-weight less reliable supervision during training.

If this is right

  • CSU raises tagging accuracy on AudioSet and controlled benchmarks across multiple model architectures.
  • The framework simultaneously handles spurious additions, inter-class misassignments, and weakened label evidence.
  • Performance gains appear without any alteration to the underlying tagging network or its test-time behavior.
  • A new manually verified benchmark called ESC-FreeGen50 supports evaluation on mixed real and generated audio.

Where Pith is reading between the lines

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

  • The same per-class reliability weighting could transfer to image or text classification tasks that use noisy multi-label supervision.
  • The learned scalars might identify which sound classes suffer the most from annotation errors in a given dataset.
  • CSU could be stacked with existing noise-robust losses to produce additive gains on very large weak-label collections.

Load-bearing premise

A single scalar per class can adequately capture and correct the combined effects of different label problems without creating new biases or requiring changes to the model.

What would settle it

Training a standard model and a CSU-augmented model on a dataset whose labels are known to be complete and accurate; if CSU yields equal or lower performance, the per-class modeling adds no benefit or introduces unnecessary bias.

Figures

Figures reproduced from arXiv: 2605.17512 by Jian Guan, Qiaoqiao Ren, Stephen Roberts, Tong Ye, Wenwu Wang, Yuanbo Hou, Zhaoyi Liu.

Figure 1
Figure 1. Figure 1: The data generation pipeline used to construct ESC-FreeGen50. [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Using siren as an example, the figure compares two strategies for audio generation. The LLM￾based strategy expands the label into a descriptive prompt, where Qwen2.5-72B-Instruct is used for prompt expansion, with system prompt settings released on the homepage. AudioLDM2 [20] is used as the current generator. The LLM-based prompt yields a clearer rhythmic pattern in the spectrogram, which is more consiste… view at source ↗
Figure 3
Figure 3. Figure 3: Baseline performance under three supervision unreliability types. Each subplot shows mAP for [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Relative performance change of models under 50% corruption, normalised to the clean training [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Score-plane loss surfaces under three 50% corruption settings. Each sample is mapped to a plane [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Summary statistics of local parameter-space geometry based on the loss landscapes in Fig. 5, [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Effective gradient multiplier trajectories learned by CSU. The x-axis denotes normalised training progress, and the y-axis denotes the class-wise median CSU multiplier 1/σ2 (log scale). The trajectories are averaged over the 10 CSU runs for each condition under 0% corruption, 50% SAN, 50% MAN, and 50% SLN, with shaded bands denoting 95% confidence intervals. those of no corruption throughout training, whic… view at source ↗
Figure 8
Figure 8. Figure 8: Kernel density estimates of the learned σ under SAN, MAN, and SLN at a 50% corruption ratio for two representative backbones: (a) MobileNet and (b) PANNs. Each curve represents the distribution of σ values across the 50 sound event classes [PITH_FULL_IMAGE:figures/full_fig_p023_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Class-wise precision of audio tagging (AT) models on the AudioSet evaluation set [1] (527 event [PITH_FULL_IMAGE:figures/full_fig_p028_9.png] view at source ↗
read the original abstract

Weakly labeled datasets such as AudioSet have driven recent progress in audio tagging. However, annotation quality varies across sound classes. Labels may be incomplete, ambiguous, or unreliable, which introduces class-dependent supervision bias during optimisation. The issue becomes harder as real and generated audio are increasingly mixed in training, and generated samples do not always match their intended semantic labels. Prior work mainly addressed unreliable supervision from missing-positive labels, while this paper targets three other sources of unreliable supervision: spurious additions, misassignments between similar classes, and weakened label evidence. These effects introduce class-dependent optimisation bias that is not explicitly modeled by most existing methods. To bridge this gap, the paper proposes a Class-wise Supervision Unreliability (CSU) framework that controls supervision strength at the class level during training. CSU learns a separate unreliability parameter for each class and down-weights less reliable supervision without changing the model architecture or inference process. To support evaluations, this paper also introduces ESC-FreeGen50, a manually verified benchmark of 50 sound classes that combines real and generated audio. Experiments on controlled benchmarks and AudioSet show that CSU improves robustness across different architectures and different sources of supervision unreliability. The results indicate that explicit class-wise modeling of supervision unreliability is an effective and practical strategy for robust audio tagging under large-scale weakly labeled training. Code and data are available at: https://github.com/Yuanbo2020/CSU

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

1 major / 2 minor

Summary. The manuscript proposes the Class-wise Supervision Unreliability (CSU) framework for robust audio tagging on weakly labeled data such as AudioSet. It explicitly models three sources of class-dependent supervision unreliability—spurious additions, misassignments between similar classes, and weakened label evidence—by learning a single scalar unreliability parameter per class that down-weights the corresponding loss term during training. The approach requires no architecture changes or inference modifications. The authors introduce the ESC-FreeGen50 benchmark (real + generated audio, 50 classes) and report performance gains on controlled benchmarks and AudioSet across multiple architectures.

Significance. If the central claim holds, the work supplies a lightweight, architecture-agnostic mechanism for mitigating class-specific label noise that is increasingly common when real and generated audio are mixed. The public release of code and the new manually verified benchmark constitute clear strengths that support reproducibility and further experimentation.

major comments (1)
  1. [§3] §3 (CSU framework description): the claim that a single scalar λ_c per class suffices to correct the combined effects of spurious additions, misassignments, and weakened evidence rests on the assumption that these distinct error sources produce gradient effects that can be absorbed by one multiplier applied to the entire per-class loss term. No derivation or low-rank analysis is provided showing that the loss geometry aligns these effects along a single axis; without such justification the learned λ_c may represent a compromise that leaves residual bias.
minor comments (2)
  1. [Abstract] Abstract: quantitative improvements (e.g., mAP deltas) and the specific architectures tested are mentioned only at high level; adding one sentence with concrete numbers would improve clarity.
  2. [Method] The description of how the unreliability parameters are initialized and regularized is brief; a short paragraph or equation clarifying the optimization objective for λ_c would aid reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We provide a point-by-point response to the major comment below.

read point-by-point responses

  1. Referee: [§3] §3 (CSU framework description): the claim that a single scalar λ_c per class suffices to correct the combined effects of spurious additions, misassignments, and weakened evidence rests on the assumption that these distinct error sources produce gradient effects that can be absorbed by one multiplier applied to the entire per-class loss term. No derivation or low-rank analysis is provided showing that the loss geometry aligns these effects along a single axis; without such justification the learned λ_c may represent a compromise that leaves residual bias.

    Authors: We acknowledge the validity of this observation. The CSU framework models the net effect of the three mentioned sources of unreliability through a single per-class scalar because each source ultimately attenuates the trustworthiness of the class label in the loss computation. Spurious additions introduce false positives, misassignments confuse similar classes, and weakened evidence reduces label strength—all of which can be approximated by down-weighting the entire loss contribution for that class. While a detailed analysis of the loss geometry (e.g., via Hessian or gradient alignment) is not included in the current manuscript, the approach is justified by its simplicity and effectiveness in practice. We will update the manuscript in §3 to explicitly state this modeling assumption and discuss potential limitations regarding residual bias. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical framework with external validation

full rationale

The paper proposes the CSU framework as a practical training modification that learns one scalar unreliability parameter per class to down-weight supervision. These parameters are optimized jointly with the model on the training data and are not derived from or equated to the reported performance metrics. Evaluations use held-out test sets on AudioSet and the newly introduced manually verified ESC-FreeGen50 benchmark; code release permits independent reproduction. No derivation chain, self-citation, or ansatz reduces the central effectiveness claim to the fitted parameters by construction. The method is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that class-dependent supervision bias can be adequately controlled by one scalar parameter per class learned from the training data itself.

free parameters (1)
  • class-wise unreliability parameter
    One scalar per sound class that modulates supervision strength; fitted during training to down-weight unreliable classes.
axioms (1)
  • domain assumption Unreliable supervision from spurious additions, misassignments, and weakened evidence introduces class-dependent optimisation bias not captured by existing methods
    Stated directly in the abstract as the motivation for CSU.

pith-pipeline@v0.9.0 · 5799 in / 1359 out tokens · 24690 ms · 2026-05-19T22:22:35.974044+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

46 extracted references · 46 canonical work pages

  1. [1]

    J. F. Gemmeke, D. P. Ellis, D. Freedman, et al., AudioSet: An ontology and human-labeled dataset for audio events, in: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017, pp. 776–780

    work page 2017

  2. [2]

    Fonseca, J

    E. Fonseca, J. Pons, X. Favory, F. Font, D. Bogdanov, et al., FSD50K: an 30 open dataset of human-labeled sound events, IEEE/ACM Transactions on Au- dio, Speech, and Language Processing 30 (2022) 829–852

    work page 2022

  3. [3]

    Y . Hou, Q. Ren, A. Mitchell, W. Wang, J. Kang, T. Belpaeme, D. Botteldooren, Soundscape captioning using sound affective quality network and large language model, IEEE Transactions on Multimedia 28 (2026) 2186–2200

    work page 2026

  4. [4]

    Fonseca, M

    E. Fonseca, M. Plakal, F. Font, D. P. Ellis, X. Serra, Audio tagging with noisy labels and minimal supervision, in: IEEE AASP DCASE 2019, 2019, p. 69

    work page 2019

  5. [5]

    Fonseca, S

    E. Fonseca, S. Hershey, M. Plakal, D. P. Ellis, et al., Addressing missing labels in large-scale sound event recognition using a teacher-student framework with loss masking, IEEE Signal Processing Letters 27 (2020) 1235–1239

    work page 2020

  6. [6]

    Fonseca, M

    E. Fonseca, M. Plakal, D. P. Ellis, F. Font, et al., Learning sound event classi- fiers from web audio with noisy labels, in: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019, pp. 21–25

    work page 2019

  7. [7]

    Iqbal, Noisy web supervision for audio classification, Ph.D

    T. Iqbal, Noisy web supervision for audio classification, Ph.D. thesis, University of Surrey (2022)

    work page 2022

  8. [8]

    Gong, Y .-A

    Y . Gong, Y .-A. Chung, J. Glass, Psla: Improving audio tagging with pretrain- ing, sampling, labeling, and aggregation, IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2021) 3292–3306

    work page 2021

  9. [9]

    A. E. Méndez Méndez, et al., Eliciting confidence for improving crowdsourced audio annotations, ACM on Human-Computer Interaction 6 (2022) 1–25

    work page 2022

  10. [10]

    Baelde, C

    M. Baelde, C. Biernacki, R. Greff, Real-time monophonic and polyphonic audio classification from power spectra, Pattern Recognition 92 (2019) 82–92

    work page 2019

  11. [11]

    Zhang, Y

    Y . Zhang, Y . Chen, C. Fang, Q. Wang, J. Wu, J. Xin, Learning from open-set noisy labels based on multi-prototype modeling, Pattern Recognition 157 (2025) 110902. 31

    work page 2025

  12. [12]

    Zhang, Y

    Q. Zhang, Y . Zhu, F. R. Cordeiro, Q. Chen, PSSCL: A progressive sample selec- tion framework with contrastive loss designed for noisy labels, Pattern Recogni- tion 161 (2025) 111284

    work page 2025

  13. [13]

    Z. Zhou, R. Li, W. Ai, X. Li, Z. Teng, B. Zhang, J. Du, Affinity-aware uncertainty quantification for learning with noisy labels, Pattern Recognition 172 (2026) 112495

    work page 2026

  14. [14]

    Y . Wang, X. Ma, Z. Chen, Y . Luo, J. Yi, J. Bailey, Symmetric cross entropy for robust learning with noisy labels, in: IEEE/CVF International Conference on Computer Vision, 2019, pp. 322–330

    work page 2019

  15. [15]

    S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, A. Rabinovich, Train- ing deep neural networks on noisy labels with bootstrapping, in: International Conference on Learning Representations, 2015

    work page 2015

  16. [16]

    M. N. Rizve, K. Duarte, Y . S. Rawat, M. Shah, In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learn- ing, in: International Conference on Learning Representations, 2020

    work page 2020

  17. [17]

    Ridnik, E

    T. Ridnik, E. Ben-Baruch, N. Zamir, A. Noy, et al., Asymmetric loss for multi- label classification, in: IEEE/CVF International Conference on Computer Vi- sion, 2021, pp. 82–91

    work page 2021

  18. [18]

    Huang, J

    Y . Huang, J. Qi, X. Wang, Z. Lin, Asymmetric polynomial loss for multi-label classification, in: IEEE International Conference on Acoustics, Speech, and Sig- nal Processing, 2023, pp. 1–5

    work page 2023

  19. [19]

    Lowne, S

    D. Lowne, S. J. Roberts, R. Garnett, Sequential non-stationary dynamic classifi- cation with sparse feedback, Pattern Recognition 43 (3) (2010) 897–905

    work page 2010

  20. [20]

    H. Liu, Y . Yuan, X. Liu, X. Mei, Q. Kong, Q. Tian, Y . Wang, W. Wang, Y . Wang, M. D. Plumbley, AudioLDM2: Learning holistic audio generation with self- 32 supervised pretraining, IEEE/ACM Transactions on Audio, Speech, and Lan- guage Processing 32 (2024) 2871–2883

    work page 2024

  21. [21]

    Ghosh, S

    S. Ghosh, S. Kumar, Z. Kong, R. Valle, et al., Synthio: Augmenting small-scale audio classification datasets with synthetic data, in: International Conference on Learning Representations, 2025

    work page 2025

  22. [22]

    Huang, J

    R. Huang, J. Huang, D. Yang, Y . Ren, L. Liu, et al., Make-an-audio: Text-to- audio generation with prompt-enhanced diffusion models, in: International Con- ference on Machine Learning, PMLR, 2023, pp. 13916–13932

    work page 2023

  23. [23]

    N. M. Müller, et al., Human perception of audio deepfakes, in: International Workshop on Deepfake Detection for Audio Multimedia, 2022, pp. 85–91

    work page 2022

  24. [24]

    K. J. Piczak, ESC: Dataset for environmental sound classification, in: ACM International Conference on Multimedia, 2015, pp. 1015–1018

    work page 2015

  25. [25]

    F. Font, G. Roma, X. Serra, Freesound technical demo, ACM International Con- ference on Multimedia (2013)

    work page 2013

  26. [26]

    Natarajan, I

    N. Natarajan, I. S. Dhillon, P. K. Ravikumar, A. Tewari, Learning with noisy labels, in: Advances in Neural Information Processing Systems, V ol. 26, 2013

    work page 2013

  27. [27]

    H. Song, M. Kim, D. Park, Y . Shin, J.-G. Lee, Learning from noisy labels with deep neural networks: A survey, IEEE Transactions on Neural Networks and Learning Systems 34 (11) (2022) 8135–8153

    work page 2022

  28. [28]

    Y . Liu, H. Cheng, K. Zhang, Identifiability of label noise transition matrix, in: International Conference on Machine Learning, PMLR, 2023, pp. 21475–21496

    work page 2023

  29. [29]

    Sandler, A

    M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen, Mobilenetv2: In- verted residuals and linear bottlenecks, in: IEEE conference on Computer Vision and Pattern Recognition, 2018, pp. 4510–4520. 33

    work page 2018

  30. [30]

    K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: IEEE conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778

    work page 2016

  31. [31]

    Q. Kong, Y . Cao, T. Iqbal, Y . Wang, W. Wang, M. D. Plumbley, PANNs: Large- scale pretrained audio neural networks for audio pattern recognition, IEEE/ACM Transactions on Audio, Speech, and Language Processing 28 (2020) 2880–2894

    work page 2020

  32. [32]

    W. Chen, Y . Liang, Z. Ma, Z. Zheng, X. Chen, EAT: self-supervised pre-training with efficient audio transformer, in: International Joint Conference on Artificial Intelligence, 2024, pp. 3807–3815

    work page 2024

  33. [33]

    Y . Hou, S. Song, C. Yu, W. Wang, et al., Audio event-relational graph represen- tation learning for acoustic scene classification, IEEE Signal Processing Letters 30 (2023) 1382–1386

    work page 2023

  34. [34]

    D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, in: Interna- tional Conference on Learning Representations, 2015

    work page 2015

  35. [35]

    Srivastava, G

    N. Srivastava, G. Hinton, et al., Dropout: A simple way to prevent neural net- works from overfitting, Journal of Machine Learning Research 15 (1) (2014) 1929–1958

    work page 2014

  36. [36]

    Frénay, M

    B. Frénay, M. Verleysen, Classification in the presence of label noise: a survey, IEEE Transactions on Neural Networks and Learning Systems 25 (5) (2013) 845–869

    work page 2013

  37. [37]

    Patrini, A

    G. Patrini, A. Rozza, et al., Making deep neural networks robust to label noise: A loss correction approach, in: IEEE conference on Computer Vision and Pattern Recognition, 2017, pp. 1944–1952

    work page 2017

  38. [38]

    Y . Hou, Q. Ren, W. Wang, D. Botteldooren, Sound-based recognition of touch 34 gestures and emotions for enhanced human-robot interaction, in: IEEE Interna- tional Conference on Acoustics, Speech, and Signal Processing, 2025, pp. 1–5

    work page 2025

  39. [39]

    B. Zhu, K. Xu, Q. Kong, H. Wang, Y . Peng, Audio tagging by cross filtering noisy labels, IEEE/ACM Transactions on Audio, Speech, and Language Pro- cessing 28 (2020) 2073–2083

    work page 2020

  40. [40]

    Y . Gong, Y . Chung, J. Glass, AST: Audio Spectrogram Transformer, in: INTER- SPEECH, 2021, pp. 571–575

    work page 2021

  41. [41]

    Nagrani, S

    A. Nagrani, S. Yang, A. Arnab, A. Jansen, C. Schmid, C. Sun, Attention bot- tlenecks for multimodal fusion, in: Advances in Neural Information Processing Systems, V ol. 34, 2021, pp. 14200–14213

    work page 2021

  42. [42]

    Guzhov, F

    A. Guzhov, F. Raue, J. Hees, A. Dengel, Audioclip: Extending clip to image, text and audio, in: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022, pp. 976–980

    work page 2022

  43. [43]

    Srivastava, Y

    S. Srivastava, Y . Wang, et al., Conformer-based self-supervised learning for non- speech audio tasks, in: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2022, pp. 8862–8866

    work page 2022

  44. [44]

    Huang, H

    P.-Y . Huang, H. Xu, J. Li, A. Baevski, M. Auli, W. Galuba, F. Metze, C. Feicht- enhofer, Masked autoencoders that listen, in: Advances in Neural Information Processing Systems, V ol. 35, 2022, pp. 28708–28720

    work page 2022

  45. [45]

    S. Chen, Y . Wu, C. Wang, S. Liu, D. Tompkins, Z. Chen, W. Che, X. Yu, F. Wei, Beats: Audio pre-training with acoustic tokenizers, in: International Conference on Machine Learning, PMLR, 2023, pp. 5178–5193

    work page 2023

  46. [46]

    Chong, H

    D. Chong, H. Wang, P. Zhou, Q. Zeng, Masked spectrogram prediction for self- supervised audio pre-training, in: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023, pp. 1–5. 35

    work page 2023