Pith. sign in

REVIEW 4 major objections 4 minor 71 references

Exploring Self-Supervised Audio Models for Generalized Anomalous Sound Detection

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A self-supervised audio model, adapted with low-rank and machine-grouping modules plus a contrastive loss, can detect anomalous sounds on unseen machine types and reaches state-of-the-art results across five public 2020-2024 benchmarks.

desk verdict A solid engineering paper whose component ablations mostly support the methods, undermined by an internally contradicted SOTA claim across all five datasets. read the letter →

arxiv 2508.12230 v1 pith:QILA5R26 submitted 2025-08-17 cs.SD eess.AS

Bing Han , Anbai Jiang , Xinhu Zheng , Wei-Qiang Zhang , Jia Liu , Pingyi Fan , Yanmin Qian This is my paper · ORCID
classification cs.SDeess.AS
keywords anomaloussounddetectionself-supervisedaudiopre-traininglow-rankadaptationmachine-awaregroupadapterdual-levelcontrastivelossunseenmachinegeneralizationfirst-shotanomalyvectorquantization
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

Machine anomaly detection usually breaks when a model meets a machine type it was never trained on, because abnormal sounds are rare and each machine sounds different. This paper claims that a self-supervised model pre-trained on general audio, not on machine noise, can be adapted so that one system detects anomalies on both seen and unseen machine types, and that this approach reaches the best published results on all five public challenge benchmarks from 2020 to 2024. The claim matters because it offers a path toward industrial monitoring that does not require per-machine retraining or large labeled anomaly collections. The paper's evidence is that pre-training helps even though the pre-training data contain almost no machine sound, that a random initialization of the same architecture collapses, and that each proposed adaptation module—a frozen-weight low-rank adapter, a machine-grouping adapter, and a contrastive loss for missing attribute labels—improves generalization on the benchmarks.

What carries the argument

The load-bearing mechanism is a three-module adapter stack placed on a frozen pre-trained transformer. The fully-connected multi-branch low-rank adapter (LoRA) generalizes the single low-rank pair $A,B$ to several branches $\{A_i\}_{i=1}^{n_l}$, $\{B_i\}_{i=1}^{n_l}$ whose inputs and outputs are aggregated, changing a layer's frozen weight $W$ into $W + \sum_{i=1}^{n_l}\sum_{j=1}^{n_l} B_i A_j$; this carries adaptation capacity while keeping the backbone frozen, which the paper finds prevents knowledge forgetting and reduces overfitting. The machine-aware group adapter computes a softmax distribution $p = \operatorname{Softmax}(g(x_{ga})/\tau_1)$ over $n_g$ learnable unit-normalized group vectors and injects their weighted sum $x_g=\sum_i p_i a_i$ into the attention branch, so different machines can activate different mixtures of group vectors within one model. The dual-level contrastive loss trains unlabeled samples by minimizing $-\log\,\frac{\exp(x_i\cdot v_c/\tau_2)}{\exp(x_i\cdot v_c/\tau_2)+\sum_{j}\exp(x_i\cdot m_j/\tau_2)}$, pulling each embedding toward its nearest vector-quantizer center $v_c$ and away from a memory bank of labeled embeddings; this dynamically forms pseudo-clusters and compensates for missing attribute labels.

What would settle it

Run the 2023 and 2024 unseen-machine evaluations with the group adapter's vectors replaced by fixed random unit vectors, or with the adapter disabled only for unseen machine types. If evaluation-set performance stays at the reported level, the group adapter is not the mechanism producing generalization; conversely, a benchmark where group assignments for unseen machines are near-uniform and performance still improves would also contradict the claimed mechanism.

Watch

Extended reading notes

Core claim

In the paper's own framing, the central discovery is that the benefit of pre-training for anomalous sound detection comes from task-agnostic self-supervised audio representations, not from parameter count or architecture: the same transformer trained from random weights performs below standard baselines, while initializing it with a masked-prediction audio backbone such as BEATs lifts performance, and the largest gains occur exactly on evaluation sets whose machine types were not seen during training. Fine-tuning this backbone with the original weights frozen and only low-rank branches trainable outperforms full fine-tuning; the paper's multi-branch low-rank structure with cross-branch connections yields an effective weight update $\Delta W = \sum_{i,j} B_i A_j$ that merges back into the frozen weights at inference. A machine-aware group adapter adds a soft weighted combination of learnable group vectors to the attention branch, letting one model capture differences across machines, and a dual-level contrastive loss replaces missing attribute labels with dynamic vector-quantized pseudo-clusters. With these components and a single K-nearest-neighbor backend, the paper reports state-of-the-art results across all five challenge datasets from 2020 through 2024.

Load-bearing premise

The load-bearing premise is that the learnable group vectors, trained only on machine types present in the training set, still organize previously unseen machine types into meaningful groups; if the weighted-sum adapter adds only noise for novel machines, the claimed generalization gains on the 2023-2024 unseen-machine evaluations collapse.

Editorial extensions

If this is right

  • If the central claim is right, a single pre-trained model can serve many machine types at once; after training, the adapter branches merge into the original weights, so inference adds no parameters and no latency.
  • Because the largest gains appear on evaluation sets with unseen machine types, first-shot anomaly detection on novel machines becomes feasible without collecting anomalies or retraining the backbone.
  • The system remains usable when attribute metadata such as operating condition is absent, since the contrastive loss derives supervision from dynamic pseudo-clusters instead of requiring complete labels.
  • Supervised or speech-oriented pre-training is not the right source of transfer; self-supervised audio pre-training on general sound is, which gives a concrete model-selection rule for future ASD systems.
  • Even a single model without ensembling is competitive with or better than ensemble systems on the same benchmarks, so the gains are attributable to the adaptation strategy rather than to combining many models.

Reading between the lines

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

  • A testable corollary the paper leaves implicit: if the group adapter works by learning a small inventory of shared acoustic modes, then increasing the number of group vectors or placing adapters at several transformer depths should produce a predictable scaling trend; the paper sweeps one placement and one scale only.
  • The same frozen-backbone, multi-branch low-rank recipe could transfer to other low-resource acoustic monitoring tasks, such as tool-wear classification or motor-fault grading, where labeled anomalous examples are scarce; the paper only evaluates binary normal-versus-anomalous detection.
  • If pre-training helps most on out-of-distribution machines, then the method should also improve performance under acoustic domain shift beyond the challenge's source-target split, for instance on recordings from a different factory floor; that setting is not directly tested here.
  • The dual-level contrastive loss could be applied to any partially labeled learning task beyond audio, since it combines a small set of labeled anchors with dynamically clustered unlabeled data; the paper does not test this.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes a framework for generalized anomalous sound detection (ASD) built on a self-supervised audio pre-trained model (BEATs), with three main contributions: fully-connected multi-branch low-rank adaptation (LoRA), a machine-aware group adapter, and a dual-level contrastive loss (DLCL) for partially unlabeled data. The method is evaluated on all five DCASE ASD challenge datasets (2020-2024) and the paper claims state-of-the-art performance across all of them. The experiments include comparisons of several pre-trained models, ablations of rank and branch count for LoRA, ablations of group adapter placement and group number, and an ablation of DLCL against static pseudo-labeling.

Significance. If the claims were fully supported, the paper would demonstrate that a single pre-trained audio encoder with parameter-efficient adapters generalizes across machine types and domains better than specialized ASD systems, which is a meaningful practical result. The strengths include the breadth of the evaluation across five challenge datasets, the controlled comparison of random versus pre-trained initialization in Table III, and the individual ablations supporting the utility of each proposed module. However, the central SOTA claim is contradicted by the paper's own Tables VII and VIII, and the presentation of the DCASE 2024 comparison is difficult to interpret because the first-place system is the authors' own ensemble. The technical components are plausible, but the headline claim requires substantial revision.

major comments (4)
  1. [Section I, Abstract, Tables VII and VIII] The claim that the proposed system 'achieves the state-of-the-art (SOTA) performance across all the ASD benchmarks, including five ASD challenges of DCASE 2020-2024' is contradicted by the paper's own tables. In Table VII (DCASE 2021), Ours has an All Hmean of 71.17, below the 2021 winner's 72.13, and on the Development set Ours (75.15) is well below the winner (78.39). In Table VIII (DCASE 2022), Ours has an All Hmean of 66.63 versus the 2022 winner's 74.67, with the largest gap on the Evaluation set (61.69 versus 70.97). The Section V-E text acknowledges that on these two challenges the system only 'remains highly competitive' and claims superiority only over previously published single systems. The unqualified SOTA claim in the abstract and contribution bullet should be removed or explicitly qualified, for example as 'SOTA among single-model systems without ensembles,' and the discrepancies should be discussed in the text.
  2. [Section V-B, Table IV] Table IV is garbled and ambiguous. The checkmarks in the Frozen and FC columns are not visually distinguishable for the three rows with LoRA Num. 8, rank 32: the rows '32 8 ✓', '32 8 ✓', and '32 8 ✓ ✓' cannot be reliably parsed, and the surrounding text does not state which configuration each row corresponds to. In particular, the statement that unfreezing the original weights 'performance declines' cannot be verified because the row with only FC checked has a higher All Hmean (64.67) than the row with only Frozen checked (64.39). The table should be reformatted with explicit Boolean labels, and the discussion of each ablation row should be mapped to a specific configuration.
  3. [Section V-E, Table X] The comparison on DCASE 2024 is misleading as presented. Table X lists '2024 No.1*' with an asterisk whose footnote says 'The first place of DCASE 2024 is our team with ensemble pre-trained models and several strategies proposed in this paper.' The paper then states that 'Ours' (a single model) achieves 'comparable performance' to this top-performing system while being below it by 1.47 Hmean points (65.50 versus 66.97). This presentation conflates the proposed single-model system with the authors' own ensemble system and obscures that the claimed overall SOTA depends on excluding their own ensemble result. The authors should clearly separate their single-model contribution from their challenge-winning ensemble and should not describe the single model as SOTA over all published systems on this benchmark.
  4. [Section V-E, Tables VII and VIII] The text claims that on DCASE 2021 and 2022 the system 'surpasses all previously published single system results,' but the evidence presented in Tables VII and VIII is insufficient to support this for the Evaluation set. For DCASE 2022, the only single-system comparison rows (MFNV2, AE, STgram, TWFRGMM) report Development-set Hmeans only, so the reader cannot verify that Ours is the best single system on the Evaluation set. For DCASE 2021, the Evaluation-set column for prior single systems is also largely empty. The authors should either add the missing Evaluation-set numbers for these prior systems or soften the claim so that it is strictly limited to the reported comparisons.
minor comments (4)
  1. [Section III-C, Eq. (6) and surrounding text] The notation for the number of groups is inconsistent: the text says 'ng denotes the number of group adapters,' while Eq. (6) is described as summing 'across nq groups' and the group representation set is said to consist of 'nq trainable parameters.' The symbols ng and nq should be used consistently, and the summation limits in Eq. (6) should be checked.
  2. [Section IV-C, Table I] The reference for MobileNet is inconsistent: the text in Section IV-C and Table I cite MobileNet [58] and [54], respectively, but reference [54] is cited as 'MobileFaceNets' while reference [58] is a CNN-based ASD system. Please align the citation keys with the intended works.
  3. [Section V-B, Table IV header] The table header 'Rank LoRA Frozen FC Hmean / Num. Dev Eval All' is broken across lines in a way that makes it unclear whether the final three columns belong to Frozen, FC, or the Hmean values. A single-row header without line breaks would remove this ambiguity.
  4. [Section VI, Conclusion] The conclusion states that the results demonstrate 'consistent and significant improvements over existing methods,' which is not accurate for DCASE 2021 and 2022 given the numbers in Tables VII and VIII. This sentence should be aligned with the qualified comparison presented in Section V-E.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the proposed modules are evaluated against external DCASE benchmarks and randomized-initialization ablations, with no claim that reduces by construction to a fitted parameter or to a self-citation.

full rationale

The paper's derivation chain is empirical rather than deductive, and I found no step where a claimed result is equivalent by construction to its input. The core assertions are that pre-trained features help ASD, that fully-connected multi-branch LoRA reduces overfitting, that the Machine-aware Group Adapter improves cross-machine generalization, and that the dual-level contrastive loss handles missing attribute labels. Each of these is supported by comparisons on the independent DCASE 2020-2024 benchmark sets and by ablations, including the random-initialization comparison in Table III, which directly tests whether gains come from pre-training rather than architecture size. The group adapter's trainable group vectors are not fitted to the evaluation outcome; they are learned on training machines and then assessed on unseen machine types, so the generalization claim is not forced by construction. The self-citations to the authors' ICASSP 2024 and Interspeech 2024 papers are contextual and disclosed, and the fact that the DCASE 2024 first-place system is the authors' own ensemble is explicitly footnoted rather than hidden; this is a self-reference, not a load-bearing circular argument. The headline claim of state-of-the-art performance across all five benchmarks is contradicted by the paper's own Tables VII and VIII (e.g., Ours 71.17 vs. 2021 No.1's 72.13; Ours 66.63 vs. 2022 No.1's 74.67), but that is an overclaim or correctness risk, not circularity. No equation in the paper defines a predicted quantity in terms of the fitted values used to produce it, and no external result is imported solely through an author-identity citation chain. Accordingly, the circularity score is 0.

Assumptions & free parameters 8 free parameters · 4 assumptions · 0 invented entities

The central empirical claims rest on three categories of assumptions: transferability of self-supervised pre-training to ASD, validity of the KNN backend and AAM classification as proxies, and transfer of the learned group prototypes and quantizer to unseen machines. Hyperparameters tuned on dev sets are listed.

free parameters (8)
  • LoRA rank r = 32
    Chosen via ablation on DCASE 2024 dev set (Table IV); best at 32.
  • LoRA branch number nl = 8
    Chosen via ablation (Table IV); best at 8.
  • Group adapter group count ng = 32
    Chosen via ablation (Table V); best at 32 in MHA position.
  • Quantizer size nq = 64
    Reported in Section IV-F; fixed at 64 for DLCL.
  • Temperature tau1 = 0.1
    Reported in Section IV-F.
  • Temperature tau2 = 0.1
    Reported in Section IV-F.
  • Loss balance lambda = 1.0
    Reported in Section IV-F.
  • Memory bank size nm = not specified
    FIFO memory bank size for negative samples in DLCL (Section III-D) is not reported.
assumptions (4)
  • domain assumption Fine-tuning a self-supervised pre-trained audio model gives useful ASD embeddings even though the pre-training data contains almost no machine sounds.
    This is the paper's main empirical premise, validated by Table III but assumed before the experiments.
  • domain assumption The KNN (k=1) cosine-distance detector on embeddings reliably separates normal and anomalous machine sounds.
    Used as the uniform backend (Section IV-E).
  • domain assumption AAM-Softmax classification on machine IDs is a useful auxiliary objective for ASD.
    Adopted from literature [46]-[48]; not derived in this paper.
  • ad hoc to paper The group representation set G trained on seen machines transfers to unseen machines.
    Core to the generalization claim of the Machine-aware Group Adapter (Section III-C, Fig. 6).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Exploring Self-Supervised Audio Models for Generalized Anomalous Sound Detection." pith.science (2026). https://pith.science/paper/QILA5R26

@misc{pith2026250812230,
  author       = {Pith},
  title        = {Pith review of: Exploring Self-Supervised Audio Models for Generalized Anomalous Sound Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QILA5R26}},
  note         = {Machine review of arXiv:2508.12230}
}
read the original abstract

Machine anomalous sound detection (ASD) is a valuable technique across various applications. However, its generalization performance is often limited due to challenges in data collection and the complexity of acoustic environments. Inspired by the success of large pre-trained models in numerous fields, this paper introduces a robust ASD model that leverages self-supervised pre-trained models trained on large-scale speech and audio datasets. Although there are inconsistencies between the pre-training datasets and the ASD task, our findings indicate that pre-training still provides substantial benefits for ASD. To mitigate overfitting and retain learned knowledge when fine-tuning with limited data, we explore Fully-Connected Low-Rank Adaptation (LoRA) as an alternative to full fine-tuning. Additionally, we propose a Machine-aware Group Adapter module, which enables the model to capture differences between various machines within a unified framework, thereby enhancing the generalization performance of ASD systems. To address the challenge of missing attribute labels, we design a novel objective function that dynamically clusters unattributed data using vector quantization and optimizes through a dual-level contrastive learning loss. The proposed methods are evaluated on all benchmark datasets, including the DCASE 2020-2024 five ASD challenges, and the experimental results show significant improvements of our new approach and demonstrate the effectiveness of our proposed strategies.

Figures

Figures reproduced from arXiv: 2508.12230 by the authors.

Figure 1
Figure 1. Overview of our proposed framework, which fine-tunes audio or speech pre-trained models using the proposed Fully [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Overview of our proposed fully-connected multi-branch [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Overview of our proposed group adapter, which is [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: An overview of the proposed Dual-Level Contrastive Loss (DLCL), which can process the anomalous sound detection [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Performance comparison visualization on DCASE 2024 [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: The visualization of Machine-aware Group Adapter [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: T-SNE visualization of machines with missing attributes from the DCASE 2024 dataset. The subfigure (a) is the [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

71 extracted references · 16 linked inside Pith

  1. [1]

    Exploring large scale pre-trained models for robust machine anomalous sound detection,

    B. Han, Z. Lv, A. Jiang, W. Huang, Z. Chen, Y . Deng, J. Ding, C. Lu, W.- Q. Zhang, P. Fan, J. Liu, and Y . Qian, “Exploring large scale pre-trained models for robust machine anomalous sound detection,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 1–5

    2024

  2. [2]

    Anopatch: Towards better consistency in machine anomalous sound detection,

    A. Jiang, B. Han, Z. Lv, Y . Deng, W.-Q. Zhang, X. Chen, Y . Qian, J. Liu, and P. Fan, “Anopatch: Towards better consistency in machine anomalous sound detection,” in Interspeech 2024, 2024, pp. 107–111

    2024

  3. [3]

    Description and discussion on dcase2020 challenge task2: Unsupervised 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 et al. , “Description and discussion on dcase2020 challenge task2: Unsupervised anomalous sound detection for machine condition monitoring,” arXiv preprint arXiv:2006.05822, 2020. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 14

    arXiv 2006

  4. [4]

    Description and discussion on dcase 2021 challenge task 2: Unsupervised anomalous sound 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 sound detection for machine condition monitoring under domain shifted conditions,” arXiv preprint arXiv:2106.04492 , 2021

    arXiv 2021

  5. [5]

    Description and discussion on dcase 2022 challenge task 2: Unsupervised 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, T. Endo, M. Yamamoto, and Y . Kawaguchi, “Description and discussion on dcase 2022 challenge task 2: Unsupervised anomalous sound detection for machine condition monitoring applying domain generalization techniques,” arXiv preprint arXiv:2206.05876 , 2022

    arXiv 2022

  6. [6]

    Description and discussion on dcase 2023 challenge task 2: First-shot unsupervised anomalous sound detection for machine condition monitoring,

    K. Dohi, K. Imoto, N. Harada, D. Niizumi, Y . Koizumi, T. Nishida, H. Purohit, R. Tanabe, T. Endo, and Y . Kawaguchi, “Description and discussion on dcase 2023 challenge task 2: First-shot unsupervised anomalous sound detection for machine condition monitoring,” arXiv preprint arXiv:2305.07828, 2023

    arXiv 2023

  7. [7]

    Descrip- tion and discussion on dcase 2024 challenge task 2: First-shot unsu- pervised anomalous sound detection for machine condition monitoring,

    T. Nishida, N. Harada, D. Niizumi, D. Albertini, R. Sannino, S. Pradolini, F. Augusti, K. Imoto, K. Dohi, H. Purohit et al., “Descrip- tion and discussion on dcase 2024 challenge task 2: First-shot unsu- pervised anomalous sound detection for machine condition monitoring,” arXiv preprint arXiv:2406.07250 , 2024

    arXiv 2024

  8. [8]

    A unifying review of deep and shallow anomaly detection,

    L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, and K.-R. M ¨uller, “A unifying review of deep and shallow anomaly detection,” Proceedings of the IEEE , vol. 109, no. 5, pp. 756–795, 2021

    2021

Show all 71 references
  1. [9]

    Anomalous sound detection based on interpolation deep neural network,

    K. Suefusa, T. Nishida, H. Purohit, R. Tanabe, T. Endo, and Y . Kawaguchi, “Anomalous sound detection based on interpolation deep neural network,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 271–275

    2020

  2. [10]

    Flow- based self-supervised density estimation for anomalous sound detection,

    K. Dohi, T. Endo, H. Purohit, R. Tanabe, and Y . Kawaguchi, “Flow- based self-supervised density estimation for anomalous sound detection,” in ICASSP 2021-2021 Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP). IEEE, 2021, pp. 336–340

    2021

  3. [11]

    Unsupervised anomalous sound detection using self- supervised classification and group masked autoencoder for density estimation,

    R. Giri, S. V . Tenneti, K. Helwani, F. Cheng, U. Isik, and A. Kr- ishnaswamy, “Unsupervised anomalous sound detection using self- supervised classification and group masked autoencoder for density estimation,” DCASE2020 Challenge, Tech. Rep., July 2020

    2020

  4. [12]

    Unsupervised anomaly detection and localization of machine audio: A gan-based approach,

    A. Jiang, W.-Q. Zhang, Y . Deng, P. Fan, and J. Liu, “Unsupervised anomaly detection and localization of machine audio: A gan-based approach,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5

    2023

  5. [13]

    Self- supervised representation learning for unsupervised anomalous sound detection under domain shift,

    H. Chen, Y . Song, L.-R. Dai, I. McLoughlin, and L. Liu, “Self- supervised representation learning for unsupervised anomalous sound detection under domain shift,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2022...

    2022

  6. [14]

    Bert: Pre-training of deep bidirectional transformers for language understanding,

    J. Devlin, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018

    2018 arXiv

  7. [15]

    Superb: Speech processing universal performance benchmark,

    S.-w. Yang, P.-H. Chi, Y .-S. Chuang, C.-I. J. Lai, K. Lakhotia, Y . Y . Lin, A. T. Liu, J. Shi, X. Chang, G.-T. Lin et al., “Superb: Speech processing universal performance benchmark,” arXiv preprint arXiv:2105.01051 , 2021

    2021 arXiv

  8. [16]

    BEATs: Audio pre-training with acoustic tokenizers,

    S. Chen, Y . Wu, C. Wang, S. Liu, D. Tompkins, Z. Chen, W. Che, X. Yu, and F. Wei, “BEATs: Audio pre-training with acoustic tokenizers,” in Proceedings of the 40th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, vol. 202. PMLR, 23–...

    2023

  9. [17]

    Large-scale self-supervised speech representation learning for automatic speaker verification,

    Z. Chen, S. Chen, Y . Wu, Y . Qian, C. Wang, S. Liu, Y . Qian, and M. Zeng, “Large-scale self-supervised speech representation learning for automatic speaker verification,” in Proc. IEEE ICASSP . IEEE, 2022, pp. 6147–6151

    2022

  10. [18]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proc. NIPS, 2017, pp. 5998–6008

    2017

  11. [19]

    Sub-cluster adacos: Learning representations for anomalous sound detection,

    K. Wilkinghoff, “Sub-cluster adacos: Learning representations for anomalous sound detection,” in 2021 International Joint Conference on Neural Networks (IJCNN) . IEEE, 2021, pp. 1–8

    2021

  12. [20]

    Diffusion augmentation sub-center modeling for unsupervised anomalous sound detection with partially attribute-unavailable conditions,

    J. Yin, Y . Gao, W. Zhang, T. Wang, and M. Zhang, “Diffusion augmentation sub-center modeling for unsupervised anomalous sound detection with partially attribute-unavailable conditions,” in ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processi...

    2025

  13. [21]

    Adaptive prototype learning for anomalous sound detection with partially known attributes,

    A. Jiang, X. Zheng, B. Han, Y . Qiu, P. Fan, W.-Q. Zhang, C. Lu, and J. Liu, “Adaptive prototype learning for anomalous sound detection with partially known attributes,” in ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEE...

    2025

  14. [22]

    Disentangling hierarchical features for anomalous sound detection under domain shift,

    J. Guan, J. Tian, Q. Zhu, F. Xiao, H. Zhang, and X. Liu, “Disentangling hierarchical features for anomalous sound detection under domain shift,” in ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2025, pp. 1–5

    2025

  15. [23]

    Joint generative-contrastive representation learning for anomalous sound detection,

    X.-M. Zeng, Y . Song, Z. Zhuo, Y . Zhou, Y .-H. Li, H. Xue, L.-R. Dai, and I. McLoughlin, “Joint generative-contrastive representation learning for anomalous sound detection,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)...

    2023

  16. [24]

    Sw-wavenet: learning represen- tation from spectrogram and wavegram using wavenet for anomalous sound detection,

    H. Chen, L. Ran, X. Sun, and C. Cai, “Sw-wavenet: learning represen- tation from spectrogram and wavegram using wavenet for anomalous sound detection,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5

    2023

  17. [25]

    Anomalous sound de- tection using self-attention-based frequency pattern analysis of machine sounds,

    H. Zhang, J. Guan, Q. Zhu, F. Xiao, and Y . Liu, “Anomalous sound de- tection using self-attention-based frequency pattern analysis of machine sounds,” in INTERSPEECH 2023, 2023, pp. 336–340

    2023

  18. [26]

    Efficient algorithms for mining outliers from large data sets,

    S. Ramaswamy, R. Rastogi, and K. Shim, “Efficient algorithms for mining outliers from large data sets,” in Proc. 2000 ACM SIGMOD Int. Conf. Manag. Data , 2000, pp. 427–438

    2000

  19. [27]

    Lof: identifying density-based local outliers,

    M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, “Lof: identifying density-based local outliers,” in Proceedings of the 2000 ACM SIGMOD international conference on Management of data , 2000, pp. 93–104

    2000

  20. [28]

    Deep autoencoding gaussian mixture model for unsupervised anomaly detection,

    B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho, and H. Chen, “Deep autoencoding gaussian mixture model for unsupervised anomaly detection,” in International conference on learning representa- tions, 2018

    2018

  21. [29]

    wav2vec 2.0: A framework for self-supervised learning of speech representations,

    A. Baevski, Y . Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” Advances in neural information processing systems, vol. 33, pp. 12 449– 12 460, 2020

    2020

  22. [30]

    Hubert: Self-supervised speech representation learning by masked prediction of hidden units,

    W.-N. Hsu, B. Bolte, Y .-H. H. Tsai, K. Lakhotia, R. Salakhutdinov, and A. Mohamed, “Hubert: Self-supervised speech representation learning by masked prediction of hidden units,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 3451–3460, 2021

    2021

  23. [31]

    Unispeech: Unified speech representation learning with labeled and unlabeled data,

    C. Wang, Y . Wu, Y . Qian, K. Kumatani, S. Liu, F. Wei, M. Zeng, and X. Huang, “Unispeech: Unified speech representation learning with labeled and unlabeled data,” in International Conference on Machine Learning. PMLR, 2021, pp. 10 937–10 947

    2021

  24. [32]

    Wavlm: Large-scale self-supervised pre- training for full stack speech processing,

    S. Chen, C. Wang, Z. Chen, Y . Wu, S. Liu, Z. Chen, J. Li, N. Kanda, T. Yoshioka, X. Xiao et al. , “Wavlm: Large-scale self-supervised pre- training for full stack speech processing,” IEEE Journal of Selected Topics in Signal Processing , vol. 16, no. 6, pp. 1505–1518, 2022

    2022

  25. [33]

    AST: Audio Spectrogram Trans- former,

    Y . Gong, Y .-A. Chung, and J. Glass, “AST: Audio Spectrogram Trans- former,” in Proc. Interspeech 2021 , 2021, pp. 571–575

    2021

  26. [34]

    An image is worth 16x16 words: Transformers for image recognition at scale,

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929 , 2020

    2010 arXiv

  27. [35]

    Imagebind: One embedding space to bind them all,

    R. Girdhar, A. El-Nouby, Z. Liu, M. Singh, K. V . Alwala, A. Joulin, and I. Misra, “Imagebind: One embedding space to bind them all,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 15 180–15 190

    2023

  28. [36]

    Self-supervised audio teacher-student transformer for both clip-level and frame-level tasks,

    X. Li, N. Shao, and X. Li, “Self-supervised audio teacher-student transformer for both clip-level and frame-level tasks,” IEEE/ACM Trans- actions on Audio, Speech, and Language Processing , 2024

    2024

  29. [37]

    Emotion recognition from speech using wav2vec 2.0 embeddings,

    L. Pepino, P. Riera, and L. Ferrer, “Emotion recognition from speech using wav2vec 2.0 embeddings,” arXiv preprint arXiv:2104.03502 , 2021

    2021 arXiv

  30. [38]

    Sparsely shared lora on whisper for child speech recognition,

    W. Liu, Y . Qin, Z. Peng, and T. Lee, “Sparsely shared lora on whisper for child speech recognition,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 11 751–11 755

    2024

  31. [39]

    Prefix-tuning: Optimizing continuous prompts for generation,

    X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” arXiv preprint arXiv:2101.00190 , 2021

    2021 arXiv

  32. [40]

    Efficient adapter transfer of self- supervised speech models for automatic speech recognition,

    B. Thomas, S. Kessler, and S. Karout, “Efficient adapter transfer of self- supervised speech models for automatic speech recognition,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2022, pp. 7102–7106

    2022

  33. [41]

    Librispeech: an asr corpus based on public domain audio books,

    V . Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: an asr corpus based on public domain audio books,” in2015 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2015, pp. 5206–5210. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. ...

    2015

  34. [42]

    Audio set: An ontology and human- labeled dataset for audio events,

    J. F. Gemmeke, D. P. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, “Audio set: An ontology and human- labeled dataset for audio events,” in 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2017,...

    2017

  35. [43]

    Attentive statistics pooling for deep speaker embedding,

    K. Okabe, T. Koshinaka, and K. Shinoda, “Attentive statistics pooling for deep speaker embedding,” arXiv preprint arXiv:1803.10963 , 2018

    2018 arXiv

  36. [44]

    Lora: Low-rank adaptation of large language models,

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” arXiv preprint arXiv:2106.09685 , 2021

    2021 arXiv

  37. [45]

    Robust anomaly sound detection framework for machine condition monitoring,

    Y . Zeng, H. Liu, L. Xu, Y . Zhou, and L. Gan, “Robust anomaly sound detection framework for machine condition monitoring,” DCASE2022 Challenge, Tech. Rep., July 2022

    2022

  38. [46]

    Arcface: Additive angular margin loss for deep face recognition,

    J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in Proc. CVPR, 2019, pp. 4690– 4699

    2019

  39. [47]

    Large margin softmax loss for speaker verification,

    Y . Liu, L. He, and J. Liu, “Large margin softmax loss for speaker verification,” in Proc. ISCA Interspeech , G. Kubin and Z. Kacic, Eds., 2019, pp. 2873–2877

    2019

  40. [48]

    Why do angular margin losses work well for semi-supervised anomalous sound detection?

    K. Wilkinghoff and F. Kurth, “Why do angular margin losses work well for semi-supervised anomalous sound detection?” IEEE/ACM Transac- tions on Audio, Speech, and Language Processing , 2023

    2023

  41. [49]

    Toyadmos: A dataset of miniature-machine operating sounds for anomalous sound detection,

    Y . Koizumi, S. Saito, H. Uematsu, N. Harada, and K. Imoto, “Toyadmos: A dataset of miniature-machine operating sounds for anomalous sound detection,” in 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) . IEEE, 2019, pp. 313–317

    2019

  42. [50]

    Toyadmos2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions,

    N. Harada, D. Niizumi, D. Takeuchi, Y . Ohishi, M. Yasuda, and S. Saito, “Toyadmos2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions,” arXiv preprint arXiv:2106.02369, 2021

    2021 arXiv

  43. [51]

    Mimii dataset: Sound dataset for malfunction- ing industrial machine investigation and inspection,

    H. Purohit, R. Tanabe, K. Ichige, T. Endo, Y . Nikaido, K. Suefusa, and Y . Kawaguchi, “Mimii dataset: Sound dataset for malfunction- ing industrial machine investigation and inspection,” arXiv preprint arXiv:1909.09347, 2019

    1909 arXiv

  44. [52]

    Mimii dg: Sound dataset for mal- functioning industrial machine investigation and inspection for domain generalization task,

    K. Dohi, T. Nishida, H. Purohit, R. Tanabe, T. Endo, M. Yamamoto, Y . Nikaido, and Y . Kawaguchi, “Mimii dg: Sound dataset for mal- functioning industrial machine investigation and inspection for domain generalization task,” arXiv preprint arXiv:2205.13879 , 2022

    2022 arXiv

  45. [53]

    Imad-ds: A dataset for industrial multi-sensor anomaly detection under domain shift conditions,

    D. Albertini, F. Augusti, K. Esmer, A. Bernardini, and R. Sannino, “Imad-ds: A dataset for industrial multi-sensor anomaly detection under domain shift conditions,” in Proceedings of the Detection and Classi- fication of Acoustic Scenes and Events 2024 Workshop (DCASE2024) , T...

    2024

  46. [54]

    Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices,

    S. Chen, Y . Liu, X. Gao, and Z. Han, “Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices,” in Biometric Recognition: 13th Chinese Conference, CCBR 2018, Urumqi, China, August 11-12, 2018, Proceedings 13 . Springer, 2018, pp. 428–438

    2018

  47. [55]

    Ensemble of complemen- tary anomaly detectors under domain shifted conditions,

    J. Lopez, G. Stemmer, and P. Lopez-Meyer, “Ensemble of complemen- tary anomaly detectors under domain shifted conditions,” DCASE2021 Challenge, Tech. Rep., July 2021

    2021

  48. [56]

    First- shot anomaly sound detection for machine condition monitoring: A do- main generalization baseline,

    N. Harada, D. Niizumi, Y . Ohishi, D. Takeuchi, and M. Yasuda, “First- shot anomaly sound detection for machine condition monitoring: A do- main generalization baseline,” in 2023 31st European Signal Processing Conference (EUSIPCO). IEEE, 2023, pp. 191–195

    2023

  49. [57]

    Specaugment: A simple data augmentation method for automatic speech recognition,

    D. S. Park, W. Chan, Y . Zhang, C. Chiu, B. Zoph, E. D. Cubuk, and Q. V . Le, “Specaugment: A simple data augmentation method for automatic speech recognition,” in Proc. ISCA Interspeech , 2019, pp. 2613–2617

    2019

  50. [58]

    Anomalous sound detection using cnn-based features by self supervised learning,

    K. Morita, T. Yano, and K. Tran, “Anomalous sound detection using cnn-based features by self supervised learning,” Tech. Rep., Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE Challenge), 2021

    2021

  51. [59]

    Ced: Consistent ensemble distillation for audio tagging,

    H. Dinkel, Y . Wang, Z. Yan, J. Zhang, and Y . Wang, “Ced: Consistent ensemble distillation for audio tagging,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024, pp. 291–295

    2024

  52. [60]

    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,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (I...

    2023

  53. [61]

    Anomalous sound detection using spectral-temporal information fusion,

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

    2022

  54. [62]

    An effective anomalous sound detection method based on represen- tation learning with simulated anomalies,

    H. Chen, Y . Song, Z. Zhuo, Y . Zhou, Y .-H. Li, H. Xue, and I. McLough- lin, “An effective anomalous sound detection method based on represen- tation learning with simulated anomalies,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processi...

    2023

  55. [63]

    Time- weighted frequency domain audio representation with gmm estimator for anomalous 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 anomalous sound detection,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5

    2023

  56. [64]

    Anomalous sound detection based on self-supervised learning,

    J. Jie, “Anomalous sound detection based on self-supervised learning,” DCASE2023 Challenge, Tech. Rep., June 2023

    2023

  57. [65]

    Self-supervised learning for anomalous sound detec- tion,

    K. Wilkinghoff, “Self-supervised learning for anomalous sound detec- tion,” arXiv preprint arXiv:2312.09578 , 2023

    2023 arXiv

  58. [66]

    Stream-based active learning for anomalous sound detection in machine condition monitoring,

    T. V . Ho, K. Dohi, and Y . Kawaguchi, “Stream-based active learning for anomalous sound detection in machine condition monitoring,” in Interspeech 2024, 2024, pp. 102–106

    2024

  59. [67]

    A dual-path frame- work with frequency-and-time excited network for anomalous sound detection,

    Y . Zhang, J. Liu, Y . Tian, H. Liu, and M. Li, “A dual-path frame- work with frequency-and-time excited network for anomalous sound detection,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2024, pp. 1266–1270

    2024

  60. [68]

    Aithu system for first-shot unsupervised anomalous sound detection,

    Z. Lv, A. Jiang, B. Han, Y . Liang, Y . Qian, X. Chen, J. Liu, and P. Fan, “Aithu system for first-shot unsupervised anomalous sound detection,” DCASE2024 Challenge, Tech. Rep., June 2024

    2024

  61. [69]

    Thuee system for first-shot unsupervised anomalous sound detection,

    A. Jiang, X. Zheng, Y . Qiu, W. Zhang, B. Chen, P. Fan, W.-Q. Zhang, C. Lu, and J. Liu, “Thuee system for first-shot unsupervised anomalous sound detection,” DCASE2024 Challenge, Tech. Rep., June 2024

    2024

  62. [70]

    Enhanced unsupervised anomalous sound detection using conditional autoencoder for machine condition monitoring,

    R. Zhao, K. Ren, and L. Zou, “Enhanced unsupervised anomalous sound detection using conditional autoencoder for machine condition monitoring,” DCASE2024 Challenge, Tech. Rep., June 2024

    2024

  63. [71]

    Smote: synthetic minority over-sampling technique,

    N. V . Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “Smote: synthetic minority over-sampling technique,” Journal of artificial intel- ligence research, vol. 16, pp. 321–357, 2002

    2002

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.