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Paper Citation Record · LEDGER

A Foundation Model for Music Informatics

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2311.03318.

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

pith.paper-citation-record.v1
2311.03318 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:40:22.124729Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c36719da-4d2a-4203-b55d-f361c62d0894 · inbound

MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization cites this paper.

MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization A Foundation Model for Music Informatics

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-10T22:40:22.124729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:40:22.124729Z digest=sha256:3387391bcf765c99b7f93d796707d943970c047b7aa249e73bc5cbbf15a703fc

Observation f1945d7b-1e6b-47e4-bd1b-aac6b4dfd18a · inbound

Layer-wise Investigation of Large-Scale Self-Supervised Music Representation Models cites this paper.

Layer-wise Investigation of Large-Scale Self-Supervised Music Representation Models A Foundation Model for Music Informatics

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T15:05:55.604317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:05:55.604317Z digest=sha256:6b1ae836cdb3115b4ca459e02a0e2c4fa1f2227ac9eaf874616c687e1fa96990

Observation 9aa95354-f7a8-41a8-bff8-e55beef666d3 · inbound

Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity cites this paper.

Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity A Foundation Model for Music Informatics

Reference 60

Resolution
verified exact
arxiv_id, observed 2026-05-21T11:40:03.156967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-21T11:36:06.967549Z digest=sha256:57629005e3965a9ebc7c28864ea85f0552fc0184e02f662bb47ff71e063d82a4

Observation 83d276eb-9364-4ecb-afe3-86671c9f0224 · inbound

Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models cites this paper.

Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models A Foundation Model for Music Informatics

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-16T02:40:30.830145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-16T02:39:46.434831Z digest=sha256:da00d7d163574615ffdf591b1450a816acc4190230f2234d7cbf8a12911900bf

Observation ed00b1c8-9247-408c-b7b4-a4d31acfa53f · inbound

Adopting State-of-the-Art Pretrained Audio Representations for Music Recommender Systems cites this paper.

Adopting State-of-the-Art Pretrained Audio Representations for Music Recommender Systems A Foundation Model for Music Informatics

Reference 96

Resolution
verified exact
arxiv_id, observed 2026-05-11T21:01:13.357851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-08T07:17:42.922029Z digest=sha256:8bd45dee85cf1c725e77e8c81acd04e3dd52aaa6bd56ed99d9a308b76392f53e