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Paper Citation Record · LEDGER

PianoVAM: A Multimodal Piano Performance Dataset

As of 17 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:2509.08800.

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

pith.paper-citation-record.v1
2509.08800 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T20:10:28.815628Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T20:10:28.639038Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

37 of 37 outbound references displayed

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  • unresolved36
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  • malformed identifier1
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Outbound references

Observation 964ebae7-ac1f-476f-a4ab-02bd470b67f9 · outbound

This paper cites PianoVAM: A Multimodal Piano Performance Dataset.

PianoVAM: A Multimodal Piano Performance Dataset PianoVAM: A Multimodal Piano Performance Dataset

Reference 1

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Observation 130c9273-fe3a-4f95-983c-176b736ffe0b · outbound

This paper cites an unresolved cited work.

PianoVAM: A Multimodal Piano Performance Dataset Unresolved cited work

Reference 2

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Observation e06943d8-dcc6-4b63-8fc1-c82a2b9ceb09 · outbound

This paper cites 3.1.1 Acquisition Workflow The acquisition workflow comprises the following steps.

PianoVAM: A Multimodal Piano Performance Dataset 3.1.1 Acquisition Workflow The acquisition workflow comprises the following steps

Reference 3

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Observation 479029ee-adcd-4d06-bc8c-22bca00587b7 · outbound

This paper cites an unresolved cited work.

PianoVAM: A Multimodal Piano Performance Dataset Unresolved cited work

Reference 4

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Observation 2ab4bd4d-121a-43c4-8dec-6fdc4096b406 · outbound

This paper cites The algorithm first processes performance videos to map hand landmarks to potential finger candidates for each MIDI note.

PianoVAM: A Multimodal Piano Performance Dataset The algorithm first processes performance videos to map hand landmarks to potential finger candidates for each MIDI note

Reference 5

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Observation 28e94f89-c224-403d-8168-0ce4ce341eb4 · outbound

This paper cites The audio-visual experiments are specifically designed to assess the visual modality’s contribution to enhancing performance under challenging acoustic conditions.

PianoVAM: A Multimodal Piano Performance Dataset The audio-visual experiments are specifically designed to assess the visual modality’s contribution to enhancing performance under challenging acoustic conditions

Reference 6

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Observation 9d330d66-2e51-4b24-b903-73e5b5562438 · outbound

This paper cites While this approach streamlines data acquisition, the dataset exhibits biases in performer identity, pedal usage, and composer representa- tion.

PianoVAM: A Multimodal Piano Performance Dataset While this approach streamlines data acquisition, the dataset exhibits biases in performer identity, pedal usage, and composer representa- tion

Reference 7

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Observation bdc06b54-5473-42fd-b41a-a739d1ec05be · outbound

This paper cites an unresolved cited work.

PianoVAM: A Multimodal Piano Performance Dataset Unresolved cited work

Reference 8

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Observation 70693e38-78a4-4dcb-b710-38ba3bbe857f · outbound

This paper cites KH2023-235).

PianoVAM: A Multimodal Piano Performance Dataset KH2023-235)

Reference 9

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Observation 075ea472-648f-4e77-9ee9-7be1c2d7fbef · outbound

This paper cites This research was supported by the National Research Foundation of Ko- rea (NRF) funded by the Korea Government (MSIT) under Grant RS-2023-NR077289 and Grant RS-2024-00358448.

PianoVAM: A Multimodal Piano Performance Dataset This research was supported by the National Research Foundation of Ko- rea (NRF) funded by the Korea Government (MSIT) under Grant RS-2023-NR077289 and Grant RS-2024-00358448

Reference 10

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Observation 8cc3f061-05cb-42f4-90ab-6aec9944d7b6 · outbound

This paper cites Hearing and seeing musical expression,.

PianoVAM: A Multimodal Piano Performance Dataset Hearing and seeing musical expression,

Reference 11

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Observation e45a5372-11d2-40dc-9bad-100072cd9547 · outbound

This paper cites When the eye listens: A meta- analysis of how audio-visual presentation enhances the appreciation of music performance,.

PianoVAM: A Multimodal Piano Performance Dataset When the eye listens: A meta- analysis of how audio-visual presentation enhances the appreciation of music performance,

Reference 12

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Observation 498b406c-95ef-4508-ac8a-905e7c9ef5ef · outbound

This paper cites Cre- ating a multitrack classical music performance dataset for multimodal music analysis: Challenges, insights, and applications,.

PianoVAM: A Multimodal Piano Performance Dataset Cre- ating a multitrack classical music performance dataset for multimodal music analysis: Challenges, insights, and applications,

Reference 13

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Observation a212f02a-763a-4cf2-b389-ddedf14b862a · outbound

This paper cites Audiovisual analysis of music perfor- mances: Overview of an emerging field,.

PianoVAM: A Multimodal Piano Performance Dataset Audiovisual analysis of music perfor- mances: Overview of an emerging field,

Reference 14

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Observation cd4a110f-970f-4e44-af6a-7da794954850 · outbound

This paper cites Sight to sound: An end-to-end approach for visual piano transcription,.

PianoVAM: A Multimodal Piano Performance Dataset Sight to sound: An end-to-end approach for visual piano transcription,

Reference 15

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Observation 7e9dde21-1c57-485e-ad1a-59baafbd79db · outbound

This paper cites A crnn- gcn piano transcription model based on audio and skele- ton features,.

PianoVAM: A Multimodal Piano Performance Dataset A crnn- gcn piano transcription model based on audio and skele- ton features,

Reference 16

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Observation e4b311b7-a105-4823-b065-3063cb4308a8 · outbound

This paper cites Audiovisual source associa- tion for string ensembles through multi-modal vibrato analysis,.

PianoVAM: A Multimodal Piano Performance Dataset Audiovisual source associa- tion for string ensembles through multi-modal vibrato analysis,

Reference 17

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Observation d8c293d1-aff6-462d-a191-e264c9922483 · outbound

This paper cites A cap- pella: Audio-visual singing voice separation,.

PianoVAM: A Multimodal Piano Performance Dataset A cap- pella: Audio-visual singing voice separation,

Reference 18

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Observation 72e34c64-5ced-4c36-afae-fd56884a2c14 · outbound

This paper cites Identifying melodic motifs and stable notes from gestural informa- tion in indian vocal performances,.

PianoVAM: A Multimodal Piano Performance Dataset Identifying melodic motifs and stable notes from gestural informa- tion in indian vocal performances,

Reference 19

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Observation 958d63e9-86ef-4131-ae13-37ccf668433a · outbound

This paper cites Enabling factorized piano music modeling and generation with the MAESTRO dataset,.

PianoVAM: A Multimodal Piano Performance Dataset Enabling factorized piano music modeling and generation with the MAESTRO dataset,

Reference 20

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Observation 154fa59a-a7df-4d63-8e36-2179d8006208 · outbound

This paper cites Maps - a piano database for multipitch estimation and automatic transcription of music,.

PianoVAM: A Multimodal Piano Performance Dataset Maps - a piano database for multipitch estimation and automatic transcription of music,

Reference 21

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Observation 94fc33e2-5136-45fd-8d21-8f0bb28dd819 · outbound

This paper cites Statistical learn- ing and estimation of piano fingering,.

PianoVAM: A Multimodal Piano Performance Dataset Statistical learn- ing and estimation of piano fingering,

Reference 22

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Observation 310a130e-ebcd-4748-bb57-670cd37d4764 · outbound

This paper cites Automatic piano fingering from partially an- notated scores using autoregressive neural networks,.

PianoVAM: A Multimodal Piano Performance Dataset Automatic piano fingering from partially an- notated scores using autoregressive neural networks,

Reference 23

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Observation 579dc6d4-0886-4450-9419-669ad5656f6c · outbound

This paper cites Onsets and frames: Dual-objective piano transcription,.

PianoVAM: A Multimodal Piano Performance Dataset Onsets and frames: Dual-objective piano transcription,

Reference 24

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Observation 3716dc2f-9ea8-4f7d-8335-c50aeb62fdad · outbound

This paper cites High- resolution piano transcription with pedals by regressing onset and offset times,.

PianoVAM: A Multimodal Piano Performance Dataset High- resolution piano transcription with pedals by regressing onset and offset times,

Reference 25

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Observation e46bf531-8af9-4043-af7c-1d685a80753f · outbound

This paper cites Hppnet: Modeling the harmonic structure and pitch invariance in piano transcription,.

PianoVAM: A Multimodal Piano Performance Dataset Hppnet: Modeling the harmonic structure and pitch invariance in piano transcription,

Reference 26

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Observation b940e44e-5279-4361-8ce0-5a79a698c595 · outbound

This paper cites Scoring time intervals using non- hierarchical transformer for automatic piano transcrip- tion,.

PianoVAM: A Multimodal Piano Performance Dataset Scoring time intervals using non- hierarchical transformer for automatic piano transcrip- tion,

Reference 27

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Observation e3a010c2-ad2b-4566-8242-b74d699f9366 · outbound

This paper cites A data-driven analysis of robust automatic piano transcription,.

PianoVAM: A Multimodal Piano Performance Dataset A data-driven analysis of robust automatic piano transcription,

Reference 28

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Observation 8ee34163-3fdd-4261-835a-6b6359cdf65b · outbound

This paper cites Automatic piano music transcription using audio-visual features,.

PianoVAM: A Multimodal Piano Performance Dataset Automatic piano music transcription using audio-visual features,

Reference 29

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Observation 03a36bc5-43bf-471f-b833-6922cd34c4c2 · outbound

This paper cites An audio-visual fusion piano transcription approach based on strategy,.

PianoVAM: A Multimodal Piano Performance Dataset An audio-visual fusion piano transcription approach based on strategy,

Reference 30

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Observation f8f0f161-50f0-4567-868f-8b646cf72239 · outbound

This paper cites A two-stage audio-visual fusion piano transcription model based on the attention mechanism,.

PianoVAM: A Multimodal Piano Performance Dataset A two-stage audio-visual fusion piano transcription model based on the attention mechanism,

Reference 31

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Observation a7a41f6a-88f5-4923-bde4-844b26dc220c · outbound

This paper cites pyloudnorm: A simple yet flexible loudness meter in python,.

PianoVAM: A Multimodal Piano Performance Dataset pyloudnorm: A simple yet flexible loudness meter in python,

Reference 32

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Observation c994a984-933c-415d-8984-045e538233c7 · outbound

This paper cites MediaPipe Hands: On-device Real-time Hand Tracking.

PianoVAM: A Multimodal Piano Performance Dataset MediaPipe Hands: On-device Real-time Hand Tracking

Reference 33

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Observation 1c437708-224f-4e05-abdf-82e9e8bcf9a6 · outbound

This paper cites Mir_eval: A transparent implementation of common mir metrics.

PianoVAM: A Multimodal Piano Performance Dataset Mir_eval: A transparent implementation of common mir metrics

Reference 34

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Observation 20f6b00f-5052-4abe-902f-ed93f626b216 · outbound

This paper cites Towards robust transcription: Exploring noise injection strategies for training data augmentation,.

PianoVAM: A Multimodal Piano Performance Dataset Towards robust transcription: Exploring noise injection strategies for training data augmentation,

Reference 35

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Observation 839cc456-2c13-409e-8a4c-78bcd30128f7 · outbound

This paper cites ViTPose++: Vision Transformer for Generic Body Pose Estimation,.

PianoVAM: A Multimodal Piano Performance Dataset ViTPose++: Vision Transformer for Generic Body Pose Estimation,

Reference 36

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Observation a62f0ef3-e0c6-4317-939f-0234f785a273 · outbound

This paper cites Hamba: Single-view 3d hand reconstruction with graph-guided bi-scanning mamba,.

PianoVAM: A Multimodal Piano Performance Dataset Hamba: Single-view 3d hand reconstruction with graph-guided bi-scanning mamba,

Reference 37

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Pith citing papers

Observation 964ebae7-ac1f-476f-a4ab-02bd470b67f9 · inbound

PianoVAM: A Multimodal Piano Performance Dataset cites this paper.

PianoVAM: A Multimodal Piano Performance Dataset PianoVAM: A Multimodal Piano Performance Dataset

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