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Paper Citation Record · LEDGER

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences

As of 9 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2506.21921.

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

pith.paper-citation-record.v1
2506.21921 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:24:14.460323Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

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  • verified fuzzy8
  • unresolved10
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 008ac312-ccb1-45e9-8502-07c7f98c0c3e · outbound

This paper cites MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

Reference 1

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Observation 26a9b326-f388-44ce-b16e-2c3b243d6b4d · outbound

This paper cites An Introduction to Outlier Analysis,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences An Introduction to Outlier Analysis,

Reference 2

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doi, observed 2026-08-06T22:24:15.846855Z

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Observation c1c537f5-7ff9-4ef1-aa97-00b729cb72a8 · outbound

This paper cites Anomaly detection: A survey,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Anomaly detection: A survey,

Reference 3

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Observation da3ecbe6-d039-4301-9254-65e927f66c98 · outbound

This paper cites A review of novelty detection,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences A review of novelty detection,

Reference 4

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Observation 18524ac6-e65d-4280-99d7-319eda11fe96 · outbound

This paper cites Deep Learning for Anomaly Detection: A Survey.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Deep Learning for Anomaly Detection: A Survey

Reference 5

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Observation 1f5ebd17-75e0-45f3-8ca8-efab8915b16a · outbound

This paper cites Deep Learning for Anomaly Detection: A Review,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Deep Learning for Anomaly Detection: A Review,

Reference 6

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Observation 23609848-b447-426e-ba58-699c8733af5c · outbound

This paper cites Ono et al., Proceedings of the 5th Workshop on Detection and Classication of Acoustic Scenes and Events (DCASE 2020).

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Ono et al., Proceedings of the 5th Workshop on Detection and Classication of Acoustic Scenes and Events (DCASE 2020)

Reference 7

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doi, observed 2026-08-06T22:24:15.634882Z

Source-reported events for the cited work

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Observation 07b05f57-468c-4830-8558-2f84d5298075 · outbound

This paper cites an unresolved cited work.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Unresolved cited work

Reference 8

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Observation 07e97338-7aff-49ac-8851-90120fb95113 · outbound

This paper cites Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio,

Reference 9

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Observation 347dcdc4-ba33-4401-91be-5f327c03459f · outbound

This paper cites Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection,

Reference 10

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Observation 2133c538-3412-4105-b810-291a59129b4c · outbound

This paper cites ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection

Reference 11

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Observation 55245a5e-9566-4100-8df9-1b6f87909045 · outbound

This paper cites Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma,

Reference 12

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Source-reported events for the cited work

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Observation 7f396f1e-db10-4117-bfd4-b3e7f9174cfd · outbound

This paper cites Anomalous Sound Detection Using a Binary Classification Model and Class Centroids,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Anomalous Sound Detection Using a Binary Classification Model and Class Centroids,

Reference 13

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Observation 7bf322d8-0e07-4a1c-a3aa-4d5d91ec2e7c · outbound

This paper cites Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 540795b6-9d9d-4af2-bd77-0d8c28c1ab73 · outbound

This paper cites Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning,

Reference 15

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 2cd782ec-3fa2-4725-9901-44e9827a7b4a · outbound

This paper cites Learning Deep Features for One-Class Classification,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Learning Deep Features for One-Class Classification,

Reference 16

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Observation 17075b71-205a-4b67-92c8-9fdbd14826b5 · outbound

This paper cites Deep Residual Learning for Image Recognition,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Deep Residual Learning for Image Recognition,

Reference 17

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Observation 8b40899d-925a-4bb5-b745-1d000f42a431 · outbound

This paper cites Support Vector Method for Novelty Detection,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Support Vector Method for Novelty Detection,

Reference 18

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Observation 1eaaeb02-5146-4c00-b5d7-f81743ee529d · outbound

This paper cites Isolation Forest,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Isolation Forest,

Reference 19

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Observation 045c1be0-41e8-4617-99c1-cebfe5d9c4e3 · outbound

This paper cites Extreme value statistics for vibration spectra outlier detection,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Extreme value statistics for vibration spectra outlier detection,

Reference 20

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Observation 2572ca59-19fd-49c5-8502-169c183cbf81 · outbound

This paper cites DCASE2020 Challenge - DCASE.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences DCASE2020 Challenge - DCASE

Reference 21

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Observation 18f923b3-f561-44ec-adbc-529df8d3def0 · outbound

This paper cites librosa: Audio and Music Signal Analysis in Python,.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences librosa: Audio and Music Signal Analysis in Python,

Reference 22

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Observation 56234183-adb1-4d22-8a34-4d1f31fcb23e · outbound

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Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Array programming with NumPy,

Reference 23

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Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences The case against accuracy estimation for comparing induction algorithms

Reference 24

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Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences SHEWHART CONTROL CHARTS

Reference 25

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Observation c5943814-f3d3-451b-b684-7d70b68732f1 · outbound

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Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Koenker, V

Reference 26

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Observation 0f955a63-f7df-467c-a3a5-49a6017555a6 · outbound

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Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Ditzhaus, R

Reference 27

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 526b9480-2b3f-46c2-a066-c00d583712f5 · outbound

This paper cites Baumeister, M.

Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences Baumeister, M

Reference 28

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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