Paper Citation Record · LEDGER

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets

As of 7 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 1 inbound Pith citation observation for arXiv:2509.06936.

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

pith.paper-citation-record.v1
2509.06936 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:55:03.659674Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:55:03.561593Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-04T22:55:03.717732Z

Reference resolution

28 of 28 outbound references displayed

verified exact2
verified fuzzy23
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 12e666c3-9704-45c1-8540-ae88bf149487 · outbound

This paper cites Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-04T22:55:03.723874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a7a270c-3eb3-4f5e-b9b3-9f1f6687da45 · outbound

This paper cites Low” (0,0.33), “Moderate.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Low” (0,0.33), “Moderate

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:04.026721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5d815927-1a2f-4ec2-99b3-12f321057596 · outbound

This paper cites We start from the full collection.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets We start from the full collection

Reference 3

Resolution: verified exact; doi, observed 2026-08-04T22:55:04.013747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cb953dfa-a6da-47e4-a266-df0bddbeeea1 · outbound

This paper cites ForMGPHot we use our proposed split.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets ForMGPHot we use our proposed split

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:04.000991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd3ec1cd-d120-4562-bb68-1242b6beaaf1 · outbound

This paper cites Instrument.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Instrument

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.988979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 180e3deb-eceb-4676-9f18-f0d378b106eb · outbound

This paper cites This distribution of win- ners indicates that there is no single reliable choice.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets This distribution of win- ners indicates that there is no single reliable choice

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.976864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1ff0d24e-3c2a-491f-afbf-19c5dfbf64bb · outbound

This paper cites IA y M´usica: C ´atedra en Inteligen- cia Artificial y M ´usica.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets IA y M´usica: C ´atedra en Inteligen- cia Artificial y M ´usica

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.962843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 253a4780-b460-4b41-8402-7eac92a2d171 · outbound

This paper cites Automatic tagging of audio: The state-of-the-art,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Automatic tagging of audio: The state-of-the-art,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.950523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d44bb64d-c559-491f-8068-9e5c67992f56 · outbound

This paper cites A survey of tagging techniques for music, speech and environmental sound,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets A survey of tagging techniques for music, speech and environmental sound,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.938281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6bac585e-1a56-4d9e-8a1f-13f9ff890c7d · outbound

This paper cites Three current issues in music autotagging,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Three current issues in music autotagging,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.925396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8957a392-5c0f-4b49-8013-ffe54c0ec903 · outbound

This paper cites Supervised and unsupervised learning of audio representations for music understanding,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Supervised and unsupervised learning of audio representations for music understanding,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.912328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5ca136b1-a901-4110-838c-4f6a64f914d7 · outbound

This paper cites Foundation Models for Music: A Survey.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Foundation Models for Music: A Survey

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T22:55:03.604395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6b5db43-a3ed-401e-97cc-0a5a4312bb8f · outbound

This paper cites Musical genre classification of audio signals,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Musical genre classification of audio signals,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T22:55:03.608240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fd2b9b3-9825-435b-ad0e-fc0d81462c19 · outbound

This paper cites The latin music database,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets The latin music database,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.891227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 96d81995-77fb-436a-b62d-bf13c9d5c8af · outbound

This paper cites Cross- collection evaluation for music classification tasks,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Cross- collection evaluation for music classification tasks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.880070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d054bc9a-d936-4894-bb9b-e27780045d59 · outbound

This paper cites The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T22:55:03.618998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7eeb57e8-b610-4dea-95ed-d61307eb1c22 · outbound

This paper cites Faults in the latin music database and with its use,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Faults in the latin music database and with its use,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.868463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 95260399-3f50-4713-ad64-f16dfec148ad · outbound

This paper cites Evaluation of algorithms using games: The case of mu- sic tagging,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Evaluation of algorithms using games: The case of mu- sic tagging,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.857691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3f518fbd-b0a5-47ef-a358-c8857f84c9fe · outbound

This paper cites The mtg-jamendo dataset for automatic mu- sic tagging,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets The mtg-jamendo dataset for automatic mu- sic tagging,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.845806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b6cf0bc5-4b94-4430-a2cf-4433866ac7f2 · outbound

This paper cites Mgphot: A dataset of musicological anno- tations for popular music (1958–2022),.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Mgphot: A dataset of musicological anno- tations for popular music (1958–2022),

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.833821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a83dff5-6ba5-445b-aeff-0932a2074f65 · outbound

This paper cites Robust speech recogni- tion via large-scale weak supervision,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Robust speech recogni- tion via large-scale weak supervision,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.822426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2790c0a5-edce-4f2e-bac5-5fbe45ec15ad · outbound

This paper cites Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Large-scale contrastive language-audio pretraining with feature fusion and keyword-to-caption augmentation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.810246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation de86f960-0bd7-447f-a5ed-2fa6a8bfb9ee · outbound

This paper cites Efficient supervised training of audio transformers for music rep- resentation learning,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Efficient supervised training of audio transformers for music rep- resentation learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.798607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3712e6ec-0288-4000-bb0f-7c603cef87b9 · outbound

This paper cites Mert: Acoustic music un- derstanding model with large-scale self-supervised train- ing,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Mert: Acoustic music un- derstanding model with large-scale self-supervised train- ing,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.785698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 14ab5446-24d9-44a5-a355-c82ecc59c7fa · outbound

This paper cites A foundation model for music informatics,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets A foundation model for music informatics,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.773307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a70991f6-2ab3-4005-94b9-ddc6e8dfc844 · outbound

This paper cites OMAR-RQ: Open music audio representation model trained with multi-feature masked token prediction,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets OMAR-RQ: Open music audio representation model trained with multi-feature masked token prediction,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.760932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1cbb177c-eb55-4f39-80ae-ba2703c18845 · outbound

This paper cites Qwen2.5: A party of foundation models,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Qwen2.5: A party of foundation models,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.748447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d715fab7-8ef7-4cfd-9f09-ca44868ab883 · outbound

This paper cites Green mir?: Investigating computational cost of recent music- ai research in ismir,.

Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets Green mir?: Investigating computational cost of recent music- ai research in ismir,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T22:55:03.736626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

Observation 12e666c3-9704-45c1-8540-ae88bf149487 · inbound