Paper Citation Record · LEDGER

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection

As of 10 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2602.18777.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2602.18777 v2

Coverage vector

measured 47 of 47 reference resolution

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measured 49 of 49 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T21:56:40.644555Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation e5b9dca7-e764-4069-93d1-ef372f4884f5 · outbound

This paper cites an unresolved cited work.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Unresolved cited work

Reference 1

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Observation a7945328-9a24-439a-8660-3ccd39bfc489 · outbound

This paper cites Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection

Reference 2

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Observation 418d9fb0-f8fa-4e7a-b465-7bc8fc1ce4c4 · outbound

This paper cites Increasing the neighbor- hood sizeKtherefore assumes that this locality remains valid as the neighborhood expands.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Increasing the neighbor- hood sizeKtherefore assumes that this locality remains valid as the neighborhood expands

Reference 3

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Observation 4d85907e-89cc-4c3c-89af-e88a332cc353 · outbound

This paper cites an unresolved cited work.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Unresolved cited work

Reference 4

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Observation c4a71125-5a8f-43c7-9563-7639a9ed204a · outbound

This paper cites Datasets We evaluate performance on five publicly available datasets for semi-supervised acoustic anomaly detection.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Datasets We evaluate performance on five publicly available datasets for semi-supervised acoustic anomaly detection

Reference 5

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Observation 8d8c0ae7-227a-48ab-8413-1207f9accf2c · outbound

This paper cites Sensitivity to neighborhood size We first analyze the sensitivity of existing approaches, namely LDN and LDN+VarMin, to the choice of the neighborhood size.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Sensitivity to neighborhood size We first analyze the sensitivity of existing approaches, namely LDN and LDN+VarMin, to the choice of the neighborhood size

Reference 6

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Observation 29214b6e-c838-4da8-84dd-c567c7218f92 · outbound

This paper cites Based on this insight, we proposed CED, a lightweight mechanism that detects distance discontinuities to identify neighborhood exits and adapt score normalization ac- cordingly.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Based on this insight, we proposed CED, a lightweight mechanism that detects distance discontinuities to identify neighborhood exits and adapt score normalization ac- cordingly

Reference 7

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Observation cf64292c-34a0-4e4f-8406-ea2ef127e851 · outbound

This paper cites All scientific content, interpretations, and conclusions are the responsibility of the authors.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection All scientific content, interpretations, and conclusions are the responsibility of the authors

Reference 8

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Observation 061f586f-e47b-47a4-98d8-ada402bddf40 · outbound

This paper cites Description and discussion on DCASE 2022 Chal- lenge Task 2: Unsupervised anomalous sound detection for ma- chine condition monitoring applying domain generalization tech- niques,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Description and discussion on DCASE 2022 Chal- lenge Task 2: Unsupervised anomalous sound detection for ma- chine condition monitoring applying domain generalization tech- niques,

Reference 9

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Observation f7d96ebd-a365-4bb6-a9e8-f5d719147627 · outbound

This paper cites Description and discussion on DCASE 2023 Challenge Task 2: First-shot unsupervised anomalous sound detection for machine condition monitoring,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Description and discussion on DCASE 2023 Challenge Task 2: First-shot unsupervised anomalous sound detection for machine condition monitoring,

Reference 10

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This paper cites Description and discussion on DCASE 2024 Challenge Task 2: First-shot unsupervised anomalous sound de- tection for machine condition monitoring,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Description and discussion on DCASE 2024 Challenge Task 2: First-shot unsupervised anomalous sound de- tection for machine condition monitoring,

Reference 11

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This paper cites Description and discussion on DCASE 2025 challenge task 2: First-shot unsupervised anomalous sound detection for machine condition monitoring,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Description and discussion on DCASE 2025 challenge task 2: First-shot unsupervised anomalous sound detection for machine condition monitoring,

Reference 12

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Observation 9589ea35-05f2-4576-9d79-05da0b9f85d3 · outbound

This paper cites Self-supervised classification for detecting anomalous sounds,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Self-supervised classification for detecting anomalous sounds,

Reference 13

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Observation ba18ebae-3b1d-4c3e-a8f4-1edf53d8cf99 · outbound

This paper cites Anomalous sound detection as a simple binary classification problem with careful selection of proxy outlier examples,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Anomalous sound detection as a simple binary classification problem with careful selection of proxy outlier examples,

Reference 14

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Observation f7321d82-a0d0-446c-812f-ba87d504acac · outbound

This paper cites Sub-cluster AdaCos: Learning representations for anomalous sound detection,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Sub-cluster AdaCos: Learning representations for anomalous sound detection,

Reference 15

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Observation dd31ae27-808c-4361-9028-b310233a41f1 · outbound

This paper cites Improved domain generalization via disentangled multi-task learning in unsupervised anomalous sound detection,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Improved domain generalization via disentangled multi-task learning in unsupervised anomalous sound detection,

Reference 16

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Observation 0244f415-e336-4f6d-9066-3eecf898f0c2 · outbound

This paper cites AnoPatch: Towards better consistency in machine anomalous sound detection,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection AnoPatch: Towards better consistency in machine anomalous sound detection,

Reference 17

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Observation 762420b4-8a7d-4e1c-a37f-f17a6da7155c · outbound

This paper cites Adaptive prototype learning for anomalous sound detec- tion with partially known attributes,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Adaptive prototype learning for anomalous sound detec- tion with partially known attributes,

Reference 18

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This paper cites Zero- shot anomalous sound detection in domestic environments using large-scale pretrained audio pattern recognition models,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Zero- shot anomalous sound detection in domestic environments using large-scale pretrained audio pattern recognition models,

Reference 19

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This paper cites Deep generic representations for domain-generalized anomalous sound detection,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Deep generic representations for domain-generalized anomalous sound detection,

Reference 20

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This paper cites Towards few-shot training-free anomaly sound detection,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Towards few-shot training-free anomaly sound detection,

Reference 21

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Observation 69132f88-a92d-43cb-91c0-a27105b0e5eb · outbound

This paper cites Exploring self-supervised audio models for generalized anomalous sound detection,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Exploring self-supervised audio models for generalized anomalous sound detection,

Reference 22

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This paper cites ECHO: Frequency-aware hi- erarchical encoding for variable-length signal,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection ECHO: Frequency-aware hi- erarchical encoding for variable-length signal,

Reference 23

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Observation 0856c63e-18f2-4a24-82a4-d1d8ebbec88e · outbound

This paper cites FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation

Reference 24

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This paper cites Temporal pooling strategies for training-free anomalous sound detection with self- supervised audio embeddings,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Temporal pooling strategies for training-free anomalous sound detection with self- supervised audio embeddings,

Reference 25

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Observation 988d656a-b5e8-46ea-9dc2-0e96a5383446 · outbound

This paper cites Keeping the balance: Anomaly score calculation for domain generalization,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Keeping the balance: Anomaly score calculation for domain generalization,

Reference 26

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This paper cites Local density-based anomaly score normalization for do- main generalization,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Local density-based anomaly score normalization for do- main generalization,

Reference 27

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Observation 5333a364-cb8f-469b-b8a2-94333ac187b0 · outbound

This paper cites Handling domain shifts for anomalous sound detection: A review of DCASE-related work,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Handling domain shifts for anomalous sound detection: A review of DCASE-related work,

Reference 28

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Observation 2da77779-f6b9-4e5d-85ae-b6bdb3b7457f · outbound

This paper cites ASDKit: A toolkit for comprehensive evaluation of anomalous sound detec- tion methods,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection ASDKit: A toolkit for comprehensive evaluation of anomalous sound detec- tion methods,

Reference 29

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Observation 9a6c32a6-c8f1-4362-a586-d0a15b5158c0 · outbound

This paper cites Adjust- ing bias in anomaly scores via variance minimization for domain- generalized discriminative anomalous sound detection,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Adjust- ing bias in anomaly scores via variance minimization for domain- generalized discriminative anomalous sound detection,

Reference 30

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Observation 64cbfd8f-8b24-4499-880c-79068d396c24 · outbound

This paper cites Description and discussion on DCASE2020 Challenge Task2: Unsupervised anomalous sound detection for machine condition monitoring,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Description and discussion on DCASE2020 Challenge Task2: Unsupervised anomalous sound detection for machine condition monitoring,

Reference 31

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Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection MIMII dataset: Sound dataset for malfunc- tioning industrial machine investigation and inspection,

Reference 32

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This paper cites Toy- ADMOS: A dataset of miniature-machine operating sounds for anomalous sound detection,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Toy- ADMOS: A dataset of miniature-machine operating sounds for anomalous sound detection,

Reference 33

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Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection MIMII DG: Sound dataset for malfunctioning industrial machine investigation and inspection for domain gener- alization task,

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Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions,

Reference 35

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This paper cites ToyADMOS2+: New Toyadmos data and benchmark results of the first-shot anomalous sound event detection baseline,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection ToyADMOS2+: New Toyadmos data and benchmark results of the first-shot anomalous sound event detection baseline,

Reference 36

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This paper cites ToyADMOS2#: Yet another dataset for the DCASE2024 challenge task 2 first-shot anomalous sound detection,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection ToyADMOS2#: Yet another dataset for the DCASE2024 challenge task 2 first-shot anomalous sound detection,

Reference 37

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Observation 719c233c-256d-4c1b-8a6b-a9fc7ca843f5 · outbound

This paper cites IMAD-DS: A dataset for industrial multi-sensor anomaly detection under domain shift conditions,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection IMAD-DS: A dataset for industrial multi-sensor anomaly detection under domain shift conditions,

Reference 38

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This paper cites ToyADMOS2025: The evaluation dataset for the DCASE2025T2 first-shot unsupervised anomalous sound detec- tion for machine condition monitoring,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection ToyADMOS2025: The evaluation dataset for the DCASE2025T2 first-shot unsupervised anomalous sound detec- tion for machine condition monitoring,

Reference 39

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Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Analyzing a portion of the ROC curve,

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Observation 415a91fc-2e70-4d7a-a28c-97337d41eaa2 · outbound

This paper cites Self-supervised learning for anomalous sound detection,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Self-supervised learning for anomalous sound detection,

Reference 41

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This paper cites AdaProj: Adaptively scaled angular margin subspace pro- jections for anomalous sound detection with auxiliary classifica- tion tasks,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection AdaProj: Adaptively scaled angular margin subspace pro- jections for anomalous sound detection with auxiliary classifica- tion tasks,

Reference 42

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Observation 384cd4f6-f124-4e44-b195-3639107997a5 · outbound

This paper cites Look, listen, and learn more: Design choices for deep audio embeddings,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Look, listen, and learn more: Design choices for deep audio embeddings,

Reference 43

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Observation 6c72147c-8693-46f7-8183-529f6caab9c4 · outbound

This paper cites BEATs: Audio pre-training with acoustic tokeniz- ers,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection BEATs: Audio pre-training with acoustic tokeniz- ers,

Reference 44

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This paper cites Audio set: An ontology and human-labeled dataset for audio events,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Audio set: An ontology and human-labeled dataset for audio events,

Reference 45

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Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection EAT: self- supervised pre-training with efficient audio transformer,

Reference 46

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Observation 0274d1b4-40b7-4556-8ac2-36d07fbdff40 · outbound

This paper cites Scaling up masked audio encoder learning for general audio clas- sification,.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection Scaling up masked audio encoder learning for general audio clas- sification,

Reference 47

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Pith citing papers

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