Paper Citation Record · LEDGER

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model

As of 14 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2508.09790.

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

pith.paper-citation-record.v1
2508.09790 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T20:53:20.996955Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

36 of 36 outbound references displayed

verified exact0
verified fuzzy30
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1d68158c-6737-4204-93e6-09da14ed0fd9 · outbound

This paper cites Explicit beat structure modeling for non-negative matrix factorization-based multipitch analysis,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Explicit beat structure modeling for non-negative matrix factorization-based multipitch analysis,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:30.086528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c98a80d6-c18f-4c0c-aad1-afeea293c7a0 · outbound

This paper cites Bayesian singing transcription based on a hierarchical generative model of keys, musical notes, and f0 trajectories,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Bayesian singing transcription based on a hierarchical generative model of keys, musical notes, and f0 trajectories,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:29.772112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7c1960c7-e490-49ab-8edd-3f3ff21086d2 · outbound

This paper cites Music structure analysis based on an lstm-hsmm hybrid model.,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Music structure analysis based on an lstm-hsmm hybrid model.,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:29.378137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9169697a-0bab-457f-95b9-ea968336ff4f · outbound

This paper cites Audio-based music structure analysis: Current trends, open challenges, and applications,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Audio-based music structure analysis: Current trends, open challenges, and applications,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:28.982748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4a629697-18d0-42f3-99a8-5a3d82c2b07f · outbound

This paper cites Improving music genre classification from multi-modal prop- erties of music and genre correlations perspective,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Improving music genre classification from multi-modal prop- erties of music and genre correlations perspective,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:28.648164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 49cc7012-eb21-4d41-877d-03110796b209 · outbound

This paper cites Melody generation from lyrics with local interpretability,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Melody generation from lyrics with local interpretability,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:28.312508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e010033f-8630-425d-8c57-8a0ee4b2bd48 · outbound

This paper cites Joint beat and downbeat tracking with recurrent neural networks.,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Joint beat and downbeat tracking with recurrent neural networks.,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:27.981212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 23dffe07-6eca-4cb1-8581-b7fd28801b2d · outbound

This paper cites Beat and downbeat tracking of symbolic music data using deep recurrent neural networks,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Beat and downbeat tracking of symbolic music data using deep recurrent neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:27.632892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 914f93e7-77f8-4a2b-ade1-c0376c21ac4d · outbound

This paper cites Robust downbeat tracking using an ensemble of convolutional net- works,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Robust downbeat tracking using an ensemble of convolutional net- works,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:27.346801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ccb104c5-5641-4b95-92b5-dd08292d764a · outbound

This paper cites Joint beat and downbeat tracking based on crnn models and a comparison of using different context ranges in convolutional layers,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Joint beat and downbeat tracking based on crnn models and a comparison of using different context ranges in convolutional layers,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:27.062812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fbae4429-90b4-4f3a-be90-20f4adbc6959 · outbound

This paper cites Multi-task learning of tempo and beat: Learning one to improve the other.,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Multi-task learning of tempo and beat: Learning one to improve the other.,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:26.750761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7a9c1232-e5ec-48f7-9fc2-cca20a900731 · outbound

This paper cites Deconstruct, analyse, re- construct: How to improve tempo, beat, and downbeat estimation.,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Deconstruct, analyse, re- construct: How to improve tempo, beat, and downbeat estimation.,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:26.436874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3b673cb9-e9b3-49c3-97a7-27d3ee5c9f32 · outbound

This paper cites Modeling beats and downbeats with a time-frequency transformer,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Modeling beats and downbeats with a time-frequency transformer,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:26.162494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 54b4e82c-4c8c-42fc-b057-edf53f11451e · outbound

This paper cites Beat Transformer: Demixed Beat and Downbeat Tracking with Dilated Self-Attention.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Beat Transformer: Demixed Beat and Downbeat Tracking with Dilated Self-Attention

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T20:53:19.158554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06fbcf4f-bc34-4481-9982-85a752c81ac6 · outbound

This paper cites Transformer-based beat tracking with low-resolution encoder and high-resolution decoder.,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Transformer-based beat tracking with low-resolution encoder and high-resolution decoder.,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:25.965467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a3fd1dbd-7bfb-42a3-a505-2fc7e3a3c1a5 · outbound

This paper cites Side-tuning: a baseline for network adaptation via additive side networks,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Side-tuning: a baseline for network adaptation via additive side networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:25.563914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d843c90c-d8d8-4e2c-98c5-99ddc8ec818f · outbound

This paper cites Compacter: Efficient low-rank hypercomplex adapter layers,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Compacter: Efficient low-rank hypercomplex adapter layers,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:25.209744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 698569bb-06b0-40fe-a3a9-f737ebdafe85 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model LoRA: Low-Rank Adaptation of Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T20:53:19.493793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 733922c7-e768-42c4-8661-de32d0abee3d · outbound

This paper cites LoRA Dropout as a Sparsity Regularizer for Overfitting Control.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model LoRA Dropout as a Sparsity Regularizer for Overfitting Control

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T20:53:19.543142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb1ee0bf-47dd-4ed8-980d-ccd9c5d3845a · outbound

This paper cites U-beat: A multi-scale beat tracking model based on wave-u-net,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model U-beat: A multi-scale beat tracking model based on wave-u-net,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:24.908244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5b4f0614-a488-400b-8681-9231fdff6c91 · outbound

This paper cites Singing Beat Tracking With Self-supervised Front-end and Linear Transformers.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Singing Beat Tracking With Self-supervised Front-end and Linear Transformers

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T20:53:19.704105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34682997-11ce-49dc-9ec2-b2101cf3634a · outbound

This paper cites Zero- note samba: Self-supervised beat tracking,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Zero- note samba: Self-supervised beat tracking,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:24.515736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 685dacd6-a10e-42f8-ae47-41f83510b481 · outbound

This paper cites Local periodicity-based beat tracking for expressive classical piano music,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Local periodicity-based beat tracking for expressive classical piano music,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:24.226839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ed07c278-78e1-4831-a3d9-05b4d3c7767d · outbound

This paper cites Beat this! Accurate beat tracking without DBN postprocessing.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Beat this! Accurate beat tracking without DBN postprocessing

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T20:53:19.873839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6026d589-c1f3-4fdc-864d-cb18cacfce28 · outbound

This paper cites MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T20:53:19.953909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b1e3dbe-cd60-41f0-8dbb-14e83e95500d · outbound

This paper cites Mert: Acoustic music understanding model with large-scale self- supervised training,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Mert: Acoustic music understanding model with large-scale self- supervised training,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:23.942764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 63326563-2e26-4fe2-b8fd-b7133841cc9f · outbound

This paper cites A foundation model for music informatics,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model A foundation model for music informatics,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:23.607739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6ca3cfd2-c1ec-4abf-8e91-0f62e60d8295 · outbound

This paper cites Self-supervised learning with random-projection quantizer for speech recognition,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Self-supervised learning with random-projection quantizer for speech recognition,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:23.282690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 991fcbd2-acf5-4350-a9ff-c633a8507c86 · outbound

This paper cites An efficient state- space model for joint tempo and meter tracking.,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model An efficient state- space model for joint tempo and meter tracking.,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:22.997740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f412a614-da2c-4c49-9b45-562948164371 · outbound

This paper cites Evalua- tion methods for musical audio beat tracking algorithms,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Evalua- tion methods for musical audio beat tracking algorithms,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:22.764113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4a2c6a0c-31d7-43ea-bc6c-213b24a9e043 · outbound

This paper cites Rwc music database: Popular, classical and jazz music databases.,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Rwc music database: Popular, classical and jazz music databases.,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:22.608604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e131db05-7bf5-42bd-a805-ccbad5fda19e · outbound

This paper cites Particle filtering applied to musical tempo tracking,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Particle filtering applied to musical tempo tracking,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:22.367278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2750236b-86b8-4604-a644-c179776aa1dc · outbound

This paper cites Rhythmic pattern modeling for beat and downbeat tracking in musical audio.,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Rhythmic pattern modeling for beat and downbeat tracking in musical audio.,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:22.073721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 722f6518-28d5-47e1-be88-5a92d33b1953 · outbound

This paper cites The harmonix set: Beats, downbeats, and functional segment annotations of western popular music.,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model The harmonix set: Beats, downbeats, and functional segment annotations of western popular music.,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:21.700680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 75efa858-e3cb-4366-9e87-4ba02ef67fc7 · outbound

This paper cites Selective sampling for beat tracking evaluation,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Selective sampling for beat tracking evaluation,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:21.401962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 678d3fab-1b7f-4acd-b663-7e92889008c3 · outbound

This paper cites Swing ratio estimation,.

BeatFM: Improving Beat Tracking with Pre-trained Music Foundation Model Swing ratio estimation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:53:21.225219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.