Disentangling Hierarchical Features for Anomalous Sound Detection Under Domain Shift

Feiyang Xiao; Hejing Zhang; Jian Guan; Jiantong Tian; Qiaoxi Zhu; Xubo Liu

REVIEW 3 major objections 6 minor 22 references

Disentangling Hierarchical Features for Anomalous Sound Detection Under Domain Shift

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper argues that adversarially disentangling domain-related from domain-unrelated audio features, guided by hierarchical metadata labels, improves anomalous sound detection under domain shift; on the DCASE 2022 Challenge Task 2…

desk verdict A plausible incremental GRL-based extension of HMIC-AGC whose reported gain is undermined by per-machine tuning on the evaluation set and an unspecified anomaly scoring rule. read the letter →

arxiv 2501.01604 v1 pith:BBWXRIW7 submitted 2025-01-03 cs.SD eess.AS

Jian Guan , Jiantong Tian , Qiaoxi Zhu , Feiyang Xiao , Hejing Zhang , Xubo Liu This is my paper · ORCID

classification cs.SDeess.AS

keywords anomaloussounddetectiondomainshiftgradientreversalfeaturedisentanglementself-supervisedlearninghierarchicalmetadataDCASE2022ChallengeTask2machineconditionmonitoring

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Anomalous sound detection systems that train on normal machine sounds often fail when the machine operates under new conditions, and this paper argues that the failure is made worse by domain-unrelated information tangled up with the attributes that define a domain. The proposed GRHD method inserts a gradient reversal layer into a hierarchical metadata-constrained feature extractor, forcing the coarse audio representation to stop encoding attribute-group identity and then refining the remainder into section- and attribute-specific features. On the DCASE 2022 Challenge Task 2 development set, this raises the harmonic-average AUC from 65.93 for the backbone to 74.72, and the ablation attributes most of the gain to the gradient reversal classifier. If the claim holds, the practical lesson is that adversarial removal of domain-unrelated information, not just stronger domain classifiers, is what improves detection accuracy under domain shift.

What carries the argument

The load-bearing mechanism is the gradient reversal classifier (GRC): an attribute-group classifier attached to the coarse embedding, with a gradient reversal layer on the path back to the backbone. The layer flips the sign of the gradient during backpropagation, so the backbone is trained to make attribute-group prediction harder at the same time the classifier tries to make it easier. This adversarial loop separates domain-unrelated information out of the coarse feature, and the hierarchical metadata branches then refine the residual into section-ID and attribute-group-specific features; Focal loss handles the imbalance across attribute groups, and the final loss is a weighted sum of the reversal, section-ID, and attribute-group terms.

What would settle it

Compute all AUCs with a single, fully specified anomaly detector on frozen embeddings—for example, a Gaussian mixture model trained on source-domain normal samples—and check whether GRHD still beats its backbone by the reported margin; if the margin collapses, the gain belongs to the scoring procedure rather than to the disentangled features.

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Extended reading notes

Core claim

The central claim is that the gap between source and target domains in anomalous sound detection is widened by domain-unrelated features entangled with the attribute information that defines a domain, and that this entanglement can be broken adversarially. GRHD adds a gradient reversal classifier to a hierarchical metadata constraint structure: the classifier tries to predict attribute groups from the coarse embedding, while the gradient reversal layer sends the opposite gradient to the backbone, forcing the embedding to become less predictive of attribute groups. The remaining coarse feature is then refined through section-ID and attribute-group branches using cross-entropy and Focal losses. On the DCASE 2022 Challenge Task 2 development set, the method reaches 84.64 source-domain AUC, 72.43 target-domain AUC, 68.82 pAUC, and 74.72 HAUC, the best harmonic average among the compared systems and clearly ahead of the 65.93 HAUC of its HMIC-AGC backbone; the ablation attributes most of the gain to the gradient reversal classifier, which raises target-domain AUC from 56.05 to 72.43.

Load-bearing premise

The reported gains assume the same anomaly scoring rule is used for every system; the paper specifies the feature-learning losses but never describes how the learned embeddings are turned into normal-versus-anomalous scores, so if that scoring is not held fixed the improvement cannot be pinned on feature disentanglement.

Editorial extensions

If this is right

Under the DCASE 2022 Task 2 evaluation protocol, GRHD raises HAUC from 65.93 for its HMIC-AGC backbone to 74.72, and the ablation shows the gradient reversal classifier accounts for most of the target-domain gain.
Target-domain AUC jumps from 56.05 without the gradient reversal classifier to 72.43 with it, an increase of roughly 16 points.
Using attribute groups as self-supervised labels outperforms using section IDs alone, supporting the paper's claim that changed attribute values are what drive domain shift.
The t-SNE visualization indicates that the adversarial stage makes latent features from different sections more aggregated and reduces the overlap between normal and anomalous sounds.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same gradient-reversal-plus-hierarchical-labels recipe is not audio-specific; any multi-source domain generalization task where metadata partitions the domain space could use it, for example speaker or environmental classification.
Because the paper leaves the anomaly scoring procedure unspecified, the cleanest test of the representation claim is to fix one detector for all embeddings and re-measure the margins.
A natural next experiment would vary the gradient-reversal strength per machine type and check whether the optimal value tracks the amount of attribute-driven shift between sections.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Guan et al. extend their earlier HMIC-AGC backbone with a gradient reversal classifier that tries to strip domain-unrelated attribute information from the coarse features, then keep the hierarchical section/attribute classifiers on top. The combination is simple and mostly sensible, and the ablation shows the GRL piece is what moves the needle on the DCASE 2022 dev set. That is the genuinely new bit: no one had grafted a GRL onto this particular hierarchical metadata structure with focal loss for the attribute classifier.

That is also where the good news mostly ends. The reported headline HAUC of 74.72 is computed on the same development set that was used to select alpha, beta, gamma per machine type "resulting in the best performance" (Section III-A.2). That is selection on the evaluation metric, which inflates any claim of superiority over baselines that did not get the same per-machine tuning. The ablation in Table II suffers from the same problem, so the +13.27 HAUC attributed to GRC is not a clean causal estimate of the component's contribution.

Equally problematic, the paper never says how the learned features are turned into anomaly scores. It specifies the feature-learning losses (Equations 2, 5, 6) but not the scoring rule that produces the AUC/pAUC values. Without that, the results are not reproducible, and the improvements cannot be cleanly attributed to the disentanglement. There are also no error bars or multiple runs, and the evaluation is confined to the development set of a single challenge task.

To give credit where it is earned: the writing is clear, the method is a legitimate integration of known components, and the self-citation to [10] is appropriate. The t-SNE visualization is suggestive but not quantitative. The stress-test concern about per-machine parameter selection holds up exactly as stated; I checked the sentence and it says what the concern says.

Who is this for? Researchers working on domain-shifted ASD who want a quick look at a plausible GRL-based variant. It should not be taken as a strong empirical result as-is. With a proper protocol—a fixed scoring rule, a held-out validation set for hyperparameter selection, multiple runs with error bars, and ideally test-set evaluation—it could become a solid workshop or short-conference paper. I would send it to peer review, but with a request for major revision rather than acceptance.

Referee Report

3 major / 6 minor

Summary. The paper proposes GRHD, a training strategy for anomalous sound detection (ASD) under domain shift. It extends the authors' previous HMIC-AGC framework [10] by inserting a gradient reversal classifier (GRC) that predicts attribute-group labels while the backbone is trained to make those labels unpredictable, thereby removing attribute-specific information from the coarse feature embedding; hierarchical constraints on section ID and attribute groups are then applied to the fine-grained features. The method uses focal loss for attribute classification and a weighted sum of three losses (Eq. 6). The paper evaluates on the development set of DCASE 2022 Challenge Task 2, reporting a HAUC of 74.72 versus 65.93 for the HMIC-AGC backbone, with an ablation study attributing most of the gain to the GRC component.

Significance. If the empirical claims survive a clean evaluation, this is a useful incremental contribution to ASD under domain shift: it provides a concrete, testable mechanism for disentangling domain-unrelated features and includes an ablation study isolating the gradient-reversal component. The paper also makes a falsifiable claim that attribute-group supervision is more effective than section-ID supervision. The strengths are the clarity of the proposed architecture, the use of a standard public benchmark, and the explicit ablation of the new component. The weakness is the empirical validation: per-machine hyperparameter selection on the same development set used for reporting, combined with an unspecified anomaly scoring rule, prevents the reported numbers from supporting the central claim as they stand.

major comments (3)

[§III-A.2, Table I] The paper states in §III-A.2 that the penalty parameters α, β, γ in Eq. (6) were 'empirically selected ... for each machine type by searching the parameters resulting in the best performance,' and Table I reports all results on this same development set. This selection-on-the-reporting-set protocol makes the headline HAUC and AUC values optimistic and breaks the comparison with the baseline systems in Table I, whose numbers were not obtained under the same per-machine search. The central claim that GRHD 'achieves the best overall performance among all systems in HAUC' is therefore not supported as presented. Please use a held-out validation split for hyperparameter selection, or fix the parameters across machine types, or report results on the DCASE 2022 evaluation set.
[§II, Table I] The paper never specifies how the learned features are converted into a normal/anomaly score for computing AUC and pAUC. Equations (1)–(6) define only the training losses; the inference procedure is absent. If the scoring rule is inherited from the HMIC-AGC backbone [10], it should be described and held fixed across all systems; otherwise the improvements reported in Table I and the ablation in Table II cannot be attributed to the proposed disentanglement, and the experiments are not reproducible.
[§III-B.2, Table II] The ablation study compares configurations with and without the gradient reversal classifier, but the text does not state whether the penalty parameters α, β, γ and the gradient reversal intensity λ were re-selected for each ablated configuration. If the parameters were tuned only for the full model, the comparison is biased against the ablations; if they were re-tuned per configuration, the +16.38 absolute gain in AUC-t and +13.27 in HAUC conflates the architectural component with per-configuration hyperparameter selection. A controlled comparison with a fixed hyperparameter protocol is needed to support the claim that the gradient reversal classifier contributes most.

minor comments (6)

[§III-B.2] The text 'it provides nearly 11% improvement in the target domain' is inconsistent with Table II, which shows an AUC-t increase from 56.05 to 72.43 (an absolute gain of 16.38); the sentence should be corrected.
[§III-A.1] The sentence 'there are990 and 10 audio samples in the source and target domains, respectively' is ambiguous and likely misstates the DCASE 2022 Task 2 data description; please clarify the sample counts for source/target and normal/anomalous splits.
[§II-A] The definition of θrev as 'the parameters of the gradient reversal' is unclear; it should specify whether this refers to the GRL scale parameter or the classifier parameters before the GRL.
[§II-A] The text says λ 'becomes stronger as the training process,' but no schedule is given; please specify how λ is annealed or, if it is held constant, state that explicitly.
[Figure 2] The input to the backbone is labeled 'Raw Waves,' but §III-A.2 describes using Log-Mel spectrograms; the figure and text should be made consistent.
[§III-B] The paper does not report the number of independent runs or variance; adding multiple seeds would strengthen the reliability of the reported improvements.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: GRHD's reported gains are empirical outcomes on an external benchmark, not consequences of the loss definitions or of a self-citation chain.

full rationale

The paper's derivation chain is a standard adversarial-disentanglement training objective. L_rev (Eq. 2) trains a gradient-reversal classifier to predict attribute-group labels while the GRL reverses gradients to the backbone, so z_rev is encouraged to be attribute-invariant by construction; L_sec and L_att (Eq. 5) then supervise section-ID and attribute-group predictive features z_sec and z_att. None of these equations entails any particular AUC value, and the reported HAUC (74.72) is an empirical measurement on the DCASE 2022 Challenge Task 2 development set against external baselines. The ablation in Table II isolates the GRC contribution, and that contribution is not logically forced: the adversarial objective could plausibly have hurt target-domain AUC, yet it improved it. Self-citations to [10] and [17] provide the backbone and hierarchical-metadata structure, but the central claim is tested against external methods and by an internal ablation, so the self-citations are not load-bearing in the sense of substituting for evidence. Two caveats are worth noting but are not circularity: Section III-A.2 states the penalty parameters alpha, beta, gamma were selected per machine type on the development set, and Tables I-II report that same development set, so the headline numbers are in-sample selected results; and the anomaly-scoring rule that converts embeddings to AUC is not specified in the paper. These are correctness and reproducibility risks, not definitional reductions of the output to the input.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The method introduces no new physical or mathematical entities. Its load-bearing assumptions are that attribute labels encode domain shift, that the hierarchical metadata structure is correct, and that gradient reversal removes only nuisance features. The free parameters are the per-machine loss weights selected on the evaluation set and the unspecified gradient reversal schedule.

free parameters (2)

alpha, beta, gamma = Not reported; selected per machine type
The loss weights in Eq. 6 were chosen by searching for the best performance on the development set, which is also the evaluation set, so the reported result is partly a selected optimum rather than a fixed prediction.
lambda (gradient reversal intensity) = Not specified
The paper states that lambda becomes stronger as training proceeds but gives no schedule or formula, leaving a key tuning choice unspecified.

assumptions (4)

domain assumption Attribute group labels (latt) are a valid proxy for domain-related information separating source and target domains.
Introduced in Section II-A where the gradient reversal classifier uses attribute groups as self-supervised labels; if attribute values do not capture the dominant domain shift, the disentanglement target is wrong.
domain assumption Section IDs and attribute groups form a correct hierarchy of domain shift types and specific domains.
Used in Section II-B with Eq. 4 and 5; based on the DCASE 2022 dataset annotation, but the paper provides no independent check of this hierarchy.
domain assumption The learned features are passed to an anomaly scorer inherited from prior work, which is never described.
The paper reports AUC and pAUC but never defines the anomaly detection score, so the evaluation assumes a working scoring rule from the cited prior paper [10].
ad hoc to paper A single gradient reversal classifier can cleanly separate domain-unrelated from domain-related coarse features.
Section II-A postulates this separation without evidence that the adversarial objective does not discard anomaly-relevant information; the t-SNE visualization only indirectly supports it.

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Pith. "Pith review of Disentangling Hierarchical Features for Anomalous Sound Detection Under Domain Shift." pith.science (2026). https://pith.science/paper/BBWXRIW7

@misc{pith2026250101604,
  author       = {Pith},
  title        = {Pith review of: Disentangling Hierarchical Features for Anomalous Sound Detection Under Domain Shift},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BBWXRIW7}},
  note         = {Machine review of arXiv:2501.01604}
}

read the original abstract

Anomalous sound detection (ASD) encounters difficulties with domain shift, where the sounds of machines in target domains differ significantly from those in source domains due to varying operating conditions. Existing methods typically employ domain classifiers to enhance detection performance, but they often overlook the influence of domain-unrelated information. This oversight can hinder the model's ability to clearly distinguish between domains, thereby weakening its capacity to differentiate normal from abnormal sounds. In this paper, we propose a Gradient Reversal-based Hierarchical feature Disentanglement (GRHD) method to address the above challenge. GRHD uses gradient reversal to separate domain-related features from domain-unrelated ones, resulting in more robust feature representations. Additionally, the method employs a hierarchical structure to guide the learning of fine-grained, domain-specific features by leveraging available metadata, such as section IDs and machine sound attributes. Experimental results on the DCASE 2022 Challenge Task 2 dataset demonstrate that the proposed method significantly improves ASD performance under domain shift.

Figures

Figures reproduced from arXiv: 2501.01604 by the authors.

**Figure 1.** Illustrations of (a) the hierarchical metadata structure accompanying the audio data for anomalous sound detection, and (b) the latent space of the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** The proposed GRHD method’s training process. The gradient reversal classifier [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** The t-SNE visualization of the latent features with or without [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages

[10]

Hierarchical metadata information constrained self-supervised learning for anomalous sound detection under domain shift,

H. Lan, Q. Zhu, J. Guan, Y . Wei, and W. Wang, “Hierarchical metadata information constrained self-supervised learning for anomalous sound detection under domain shift,” in Proceedings of International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 7670–7674

work page 2024
[1]

Description and discussion on DCASE 2020 challenge task2: Unsu- pervised anomalous sound detection for machine condition monitoring,

Y . Koizumi, Y . Kawaguchi, K. Imoto, T. Nakamura, Y . Nikaido, R. Tan- abe, H. Purohit, K. Suefusa, T. Endo, M. Yasuda, and N. Harada, “Description and discussion on DCASE 2020 challenge task2: Unsu- pervised anomalous sound detection for machine condition monitoring,” in Proceedings of Detection and Classification of Acoustic Scenes and Events (DCASE) W...

work page 2020
[2]

First-shot unsupervised anomalous sound detection with unknown anomalies estimated by metadata-assisted audio generation,

H. Zhang, Q. Zhu, J. Guan, H. Liu, F. Xiao, J. Tian, X. Mei, X. Liu, and W. Wang, “First-shot unsupervised anomalous sound detection with unknown anomalies estimated by metadata-assisted audio generation,” in Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 1271–1275

work page 2024
[3]

Anomalous sound detection using audio representation with machine id based contrastive learning pretraining,

J. Guan, F. Xiao, Y . Liu, Q. Zhu, and W. Wang, “Anomalous sound detection using audio representation with machine id based contrastive learning pretraining,” in Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5

work page 2023
[4]

Time-weighted frequency domain audio representation with GMM estimator for anoma- lous sound detection,

J. Guan, Y . Liu, Q. Zhu, T. Zheng, J. Han, and W. Wang, “Time-weighted frequency domain audio representation with GMM estimator for anoma- lous sound detection,” in Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2023, pp. 1–5

work page 2023
[5]

Description and discussion on DCASE 2022 challenge task 2: Unsu- pervised anomalous sound detection for machine condition monitoring applying domain generalization techniques,

K. Dohi, K. Imoto, N. Harada, D. Niizumi, Y . Koizumi, T. Nishida, H. Purohit, R. Tanabe, T. Endo, M. Yamamoto, and Y . Kawaguchi, “Description and discussion on DCASE 2022 challenge task 2: Unsu- pervised anomalous sound detection for machine condition monitoring applying domain generalization techniques,” in Proceedings of Detection and Classification o...

work page 2022
[6]

Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation,

Y . Luo, L. Zheng, T. Guan, J. Yu, and Y . Yang, “Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019

work page 2019
[7]

Learning from synthetic data: Addressing domain shift for semantic segmentation,

S. Sankaranarayanan, Y . Balaji, A. Jain, S. N. Lim, and R. Chellappa, “Learning from synthetic data: Addressing domain shift for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018

work page 2018

Show all 22 references

[8]

Moment matching for multi-source domain adaptation,

X. Peng, Q. Bai, X. Xia, Z. Huang, K. Saenko, and B. Wang, “Moment matching for multi-source domain adaptation,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2019

2019
[9]

Learning attributes equals multi-source domain generalization,

C. Gan, T. Yang, and B. Gong, “Learning attributes equals multi-source domain generalization,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016

2016
[11]

Description and discussion on DCASE 2021 challenge task 2: Unsupervised anomalous detection for machine condition monitoring under domain shifted conditions,

Y . Kawaguchi, K. Imoto, Y . Koizumi, N. Harada, D. Niizumi, K. Dohi, R. Tanabe, H. Purohit, and T. Endo, “Description and discussion on DCASE 2021 challenge task 2: Unsupervised anomalous detection for machine condition monitoring under domain shifted conditions,” in Proceedi...

2021
[12]

AITHU system for unsupervised anomalous detection of machine working status via sounding,

Y . Deng, J. Liu, and W.-Q. Zhang, “AITHU system for unsupervised anomalous detection of machine working status via sounding,” DCASE 2022 Challenge, Tech. Rep., July 2022

2022
[13]

An outlier exposed anomalous sound detection system for domain generalization in machine condition monitoring,

K. Wilkinghoff, “An outlier exposed anomalous sound detection system for domain generalization in machine condition monitoring,” DCASE Challenge, Tech. Rep., 2022

2022
[14]

JLESS submission to DCASE 2022 task 2: Batch mixing strategy based method with anomaly detector for anomalous sound detection,

J. Bai, Y . Jia, and S. Huang, “JLESS submission to DCASE 2022 task 2: Batch mixing strategy based method with anomaly detector for anomalous sound detection,” DCASE Challenge, Tech. Rep., 2022

2022
[15]

The impact of frequency bands on acoustic anomaly detection of machines using deep learning based model,

T. Nguyen, L. Pham, P. Lam, D. Ngo, H. Tang, and A. Schindler, “The impact of frequency bands on acoustic anomaly detection of machines using deep learning based model,” arXiv preprint arXiv:2403.00379 , 2024

2024 arXiv
[16]

DG-Mix: Domain generalization for anomalous sound detection based on self-supervised learning

I. Nejjar, J. Meunier-Pion, G. Frusque, and O. Fink, “DG-Mix: Domain generalization for anomalous sound detection based on self-supervised learning.” in Proceedings of Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop , 2022, pp. 131–135

2022
[17]

Anomalous sound detec- tion using spectral-temporal information fusion,

Y . Liu, J. Guan, Q. Zhu, and W. Wang, “Anomalous sound detec- tion using spectral-temporal information fusion,” in Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 816–820

2022
[18]

Focal loss for dense object detection,

T.-Y . Lin, P. Goyal, R. Girshick, K. He, and P. Doll ´ar, “Focal loss for dense object detection,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2017, pp. 2980–2988

2017
[19]

Domain-adversarial training of neural networks,

Y . Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Lavio- lette, M. Marchand, and V . Lempitsky, “Domain-adversarial training of neural networks,” Journal of Machine Learning Research, vol. 17, no. 1, pp. 2096–2030, 2016

2016
[20]

Adam: A method for stochastic optimization,

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proceedings of International Conference on Learning Representations (ICLR), 2015

2015
[21]

Mixup: Beyond empirical risk minimization,

H. Zhang, M. Cisse, Y . N. Dauphin, and D. Lopez-Paz, “Mixup: Beyond empirical risk minimization,” inProceedings of International Conference on Learning Representations (ICLR) , 2018

2018
[22]

Visualizing data using t-SNE

L. Van der Maaten and G. Hinton, “Visualizing data using t-SNE.” Journal of Machine Learning Research , vol. 9, no. 11, p. 2579–2605, 2008

2008

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [10]

Hierarchical metadata information constrained self-supervised learning for anomalous sound detection under domain shift,

H. Lan, Q. Zhu, J. Guan, Y . Wei, and W. Wang, “Hierarchical metadata information constrained self-supervised learning for anomalous sound detection under domain shift,” in Proceedings of International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 7670–7674

work page 2024

[2] [1]

Description and discussion on DCASE 2020 challenge task2: Unsu- pervised anomalous sound detection for machine condition monitoring,

Y . Koizumi, Y . Kawaguchi, K. Imoto, T. Nakamura, Y . Nikaido, R. Tan- abe, H. Purohit, K. Suefusa, T. Endo, M. Yasuda, and N. Harada, “Description and discussion on DCASE 2020 challenge task2: Unsu- pervised anomalous sound detection for machine condition monitoring,” in Proceedings of Detection and Classification of Acoustic Scenes and Events (DCASE) W...

work page 2020

[3] [2]

First-shot unsupervised anomalous sound detection with unknown anomalies estimated by metadata-assisted audio generation,

H. Zhang, Q. Zhu, J. Guan, H. Liu, F. Xiao, J. Tian, X. Mei, X. Liu, and W. Wang, “First-shot unsupervised anomalous sound detection with unknown anomalies estimated by metadata-assisted audio generation,” in Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 1271–1275

work page 2024

[4] [3]

Anomalous sound detection using audio representation with machine id based contrastive learning pretraining,

J. Guan, F. Xiao, Y . Liu, Q. Zhu, and W. Wang, “Anomalous sound detection using audio representation with machine id based contrastive learning pretraining,” in Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5

work page 2023

[5] [4]

Time-weighted frequency domain audio representation with GMM estimator for anoma- lous sound detection,

J. Guan, Y . Liu, Q. Zhu, T. Zheng, J. Han, and W. Wang, “Time-weighted frequency domain audio representation with GMM estimator for anoma- lous sound detection,” in Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2023, pp. 1–5

work page 2023

[6] [5]

Description and discussion on DCASE 2022 challenge task 2: Unsu- pervised anomalous sound detection for machine condition monitoring applying domain generalization techniques,

K. Dohi, K. Imoto, N. Harada, D. Niizumi, Y . Koizumi, T. Nishida, H. Purohit, R. Tanabe, T. Endo, M. Yamamoto, and Y . Kawaguchi, “Description and discussion on DCASE 2022 challenge task 2: Unsu- pervised anomalous sound detection for machine condition monitoring applying domain generalization techniques,” in Proceedings of Detection and Classification o...

work page 2022

[7] [6]

Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation,

Y . Luo, L. Zheng, T. Guan, J. Yu, and Y . Yang, “Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019

work page 2019

[8] [7]

Learning from synthetic data: Addressing domain shift for semantic segmentation,

S. Sankaranarayanan, Y . Balaji, A. Jain, S. N. Lim, and R. Chellappa, “Learning from synthetic data: Addressing domain shift for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018

work page 2018