Paper Citation Record · LEDGER

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music

As of 5 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2607.28876.

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

pith.paper-citation-record.v1
2607.28876 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T01:30:44.177960Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T01:30:39.382777Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

verified exact0
verified fuzzy0
unresolved41
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation acb6ba8e-0196-4688-9ba6-7688464f95bb · outbound

This paper cites Learning to Predict Performance-induced Emotion Differences in Classical Piano Music.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Learning to Predict Performance-induced Emotion Differences in Classical Piano Music

Reference 1

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Observation f3b0455e-63f4-44d2-851f-f22b583e0760 · outbound

This paper cites melodiousness.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music melodiousness

Reference 2

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Observation 3fb33f90-615c-4eef-aa8f-54bc153e15fe · outbound

This paper cites an unresolved cited work.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Unresolved cited work

Reference 3

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Observation c06f0135-e872-43f3-a846-c6bbd7e23460 · outbound

This paper cites To ensure that the performance-based features capture meaningful variation across performers rather than mainly encoding piece iden- tity, we conduct the following analyses.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music To ensure that the performance-based features capture meaningful variation across performers rather than mainly encoding piece iden- tity, we conduct the following analyses

Reference 4

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Observation a0a70e93-25ea-4a08-bdca-9011c5857263 · outbound

This paper cites informa- tion.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music informa- tion

Reference 5

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Observation 04f9dd26-9c3c-481f-beab-f13542a2af1b · outbound

This paper cites give me a performance that is more positive and less aggressive than X.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music give me a performance that is more positive and less aggressive than X

Reference 6

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Observation fe5104a1-0c16-4f81-950c-16ee5f177cd6 · outbound

This paper cites an unresolved cited work.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Unresolved cited work

Reference 7

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Observation df6890e9-b537-4e7d-8914-217e23e89498 · outbound

This paper cites 101019375 (Whither Music?).

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music 101019375 (Whither Music?)

Reference 8

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Observation 85c8fb81-e9b2-45ee-9ba9-bd216317ef0e · outbound

This paper cites Computational analysis and modeling of ex- pressive timing in chopin’s mazurkas.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Computational analysis and modeling of ex- pressive timing in chopin’s mazurkas

Reference 9

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Observation 9a76d727-8d74-4fe4-9c3f-eb0b7b7f266b · outbound

This paper cites Rendering music performance with interpre- tation variations using conditional variational rnn,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Rendering music performance with interpre- tation variations using conditional variational rnn,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T01:30:41.309916Z

Source-reported events for the cited work

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Observation d86cd000-9b26-4098-8e65-f9c4ccdbccf7 · outbound

This paper cites Regression-based music emotion prediction using triplet neural networks,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Regression-based music emotion prediction using triplet neural networks,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T01:30:40.604639Z

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Observation bf954a26-0617-404c-88dd-77238e66bd2a · outbound

This paper cites Stacked convolutional and recurrent neural networks for music emotion recogni- tion,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Stacked convolutional and recurrent neural networks for music emotion recogni- tion,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T01:30:40.664653Z

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Observation bcae72dc-1e2f-4736-ad45-70a94e05a601 · outbound

This paper cites Bidirectional convolutional recurrent sparse network (BCRSN): An efficient model for music emotion recognition,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Bidirectional convolutional recurrent sparse network (BCRSN): An efficient model for music emotion recognition,

Reference 13

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Observation 8a0af520-942b-4fef-8f9a-02e9da95a4d1 · outbound

This paper cites Emotional expression in music performance: Between the performer’s inten- tion and the listener’s experience,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Emotional expression in music performance: Between the performer’s inten- tion and the listener’s experience,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T01:30:40.829883Z

Source-reported events for the cited work

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Observation 252a8888-4cc6-4929-9988-58f25bb61748 · outbound

This paper cites Decoding emotions in expressive music performances: A multi-lab replication and extension study,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Decoding emotions in expressive music performances: A multi-lab replication and extension study,

Reference 15

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Source-reported events for the cited work

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Observation 850cb288-64b3-4f5a-bcb7-f561932e759f · outbound

This paper cites Computational Modeling of Expressive Music Performance with Linear and Non- Linear Basis Function Models,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Computational Modeling of Expressive Music Performance with Linear and Non- Linear Basis Function Models,

Reference 16

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Source-reported events for the cited work

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Observation e2d75792-174e-48e1-9596-9938bfd32e49 · outbound

This paper cites Linear basis models for prediction and analysis of musical expression,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Linear basis models for prediction and analysis of musical expression,

Reference 17

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Source-reported events for the cited work

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Observation 4aaedb28-7265-45ed-a9c1-dfe57d009c1b · outbound

This paper cites An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music An evaluation of linear and non-linear models of expressive dynamics in classical piano and symphonic music,

Reference 18

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Observation 581581ac-3878-4b63-b4d6-e2dc6a68586e · outbound

This paper cites Measurement and Reproduc- tion Accuracy of Computer-controlled Grand Pianos,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Measurement and Reproduc- tion Accuracy of Computer-controlled Grand Pianos,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T01:30:41.992381Z

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Observation 425e4181-64e3-4d39-87f6-91d9a7f6164b · outbound

This paper cites A Statistical View on the Expressive Timing of Piano Rolled Chords,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music A Statistical View on the Expressive Timing of Piano Rolled Chords,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T01:30:42.088077Z

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Observation 7fd3ca29-622a-4760-924b-602ea213a229 · outbound

This paper cites Vir- tuosonet: A hierarchical rnn-based system for model- ing expressive piano performance.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Vir- tuosonet: A hierarchical rnn-based system for model- ing expressive piano performance

Reference 21

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Observation 2bb80c55-dad2-4d22-b373-805858ded4e4 · outbound

This paper cites Scoreperformer: Expressive piano performance rendering with fine-grained con- trol.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Scoreperformer: Expressive piano performance rendering with fine-grained con- trol

Reference 22

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Observation 3ee2dd41-cd00-4cb5-9ff2-aa28ac749e21 · outbound

This paper cites DExter: Learning and controlling performance expression with diffusion models,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music DExter: Learning and controlling performance expression with diffusion models,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T01:30:41.537456Z

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Observation ba9933ad-854e-49d9-ab2a-897ab92d3b68 · outbound

This paper cites The performance of music,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music The performance of music,

Reference 24

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Observation 90d4d7a7-9e01-48e7-8e14-6f36926e7e9f · outbound

This paper cites Acoustically Expressing Affect,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Acoustically Expressing Affect,

Reference 25

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Observation 724b1165-a435-4605-96ba-81a0ca802e36 · outbound

This paper cites Individualized interpretation: Exploring struc- tural and interpretive effects on evaluations of emo- tional content in bach’s well tempered clavier,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Individualized interpretation: Exploring struc- tural and interpretive effects on evaluations of emo- tional content in bach’s well tempered clavier,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T01:30:41.797604Z

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Observation 2c37afa8-9011-4ec2-a3cf-c1ff136186bf · outbound

This paper cites Beyond the notes: Clarifying the role of expressivity in conveying musical emotion,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Beyond the notes: Clarifying the role of expressivity in conveying musical emotion,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T01:30:41.824758Z

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Observation 10d05437-f5b4-4a65-a91f-69a8b02acd43 · outbound

This paper cites On perceived emotion in expressive piano performance: Further experimental evidence for the relevance of mid-level perceptual fea- tures,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music On perceived emotion in expressive piano performance: Further experimental evidence for the relevance of mid-level perceptual fea- tures,

Reference 28

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Observation 9a6e5a72-e21c-4ae8-813d-eddaae1e083c · outbound

This paper cites an unresolved cited work.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Unresolved cited work

Reference 29

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Observation 28419992-665e-4b90-ac95-9bb681ac8a3a · outbound

This paper cites Towards Musically Informed Evaluation of Pi- ano Transcription Models,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Towards Musically Informed Evaluation of Pi- ano Transcription Models,

Reference 30

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Observation f747e909-30fa-4c16-8cd6-9e3cb596d246 · outbound

This paper cites Melody Lead in Piano Performance: Ex- pressive Device or Artifact?.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Melody Lead in Piano Performance: Ex- pressive Device or Artifact?

Reference 31

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Observation cf3aac35-4503-4559-bf3a-5ce209f6a097 · outbound

This paper cites Parti- tura: A Python Package for Symbolic Music Process- ing,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Parti- tura: A Python Package for Symbolic Music Process- ing,

Reference 32

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Observation aa2ab614-38ee-4a28-8019-fce38ab22b2f · outbound

This paper cites A Data-Driven Analysis of Robust Automatic Piano Transcription.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music A Data-Driven Analysis of Robust Automatic Piano Transcription

Reference 33

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Observation e0a0aaaa-0783-4a56-acc0-1e26935b02cf · outbound

This paper cites Sound and mu- sic biases in deep music transcription models: a sys- tematic analysis,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Sound and mu- sic biases in deep music transcription models: a sys- tematic analysis,

Reference 34

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Observation 2b7c08f6-0bb4-46e6-aec7-148144d9ef13 · outbound

This paper cites ASAP: a dataset of aligned scores and per- formances for piano transcription,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music ASAP: a dataset of aligned scores and per- formances for piano transcription,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T01:30:42.762513Z

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Observation 1a58dc67-2164-4e1b-b33f-996aa4f4bdbd · outbound

This paper cites High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T01:30:42.912983Z

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Observation 9f491533-bf10-4d85-ba3f-6280643f327c · outbound

This paper cites Automatic Piano Transcrip- tion with Hierarchical Frequency-Time Transformer,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Automatic Piano Transcrip- tion with Hierarchical Frequency-Time Transformer,

Reference 37

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Observation 5645df49-dcb8-42b1-ad2d-724ea2397b04 · outbound

This paper cites Scoring Time Intervals Us- ing Non-Hierarchical Transformer for Automatic Pi- ano Transcription,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Scoring Time Intervals Us- ing Non-Hierarchical Transformer for Automatic Pi- ano Transcription,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T01:30:43.419044Z

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Observation df6c8534-006b-443d-8901-e9dc71e19fbb · outbound

This paper cites MIR_EV AL: A Transparent Implementation of Common MIR Met- rics,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music MIR_EV AL: A Transparent Implementation of Common MIR Met- rics,

Reference 39

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Observation 7eee6691-fe14-4da6-8e51-03dcd71f4242 · outbound

This paper cites Representational similarity analysis - connecting the branches of systems neuroscience,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Representational similarity analysis - connecting the branches of systems neuroscience,

Reference 41

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Observation 2904d9bd-698b-437f-a684-8d0ebda03da7 · outbound

This paper cites Rank-n-contrast: learning continuous representations for regression,.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Rank-n-contrast: learning continuous representations for regression,

Reference 42

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Observation 428a883c-47b9-4e34-af07-7454b8480c48 · outbound

This paper cites an unresolved cited work.

Learning to Predict Performance-induced Emotion Differences in Classical Piano Music Unresolved cited work

Reference 127

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Pith citing papers

Observation acb6ba8e-0196-4688-9ba6-7688464f95bb · inbound