{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:U5L6M2XJ2EC3EANNNDUXHEG3A3","short_pith_number":"pith:U5L6M2XJ","schema_version":"1.0","canonical_sha256":"a757e66ae9d105b201ad68e97390db06e934e6964b3070af74775ce3567373cb","source":{"kind":"arxiv","id":"2606.10627","version":1},"attestation_state":"computed","paper":{"title":"Profy: Interpretable Visualization of Expertise-Dependent Motor Skills Toward Supporting Piano Practice","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SD"],"primary_cat":"cs.HC","authors_text":"Fujiki Nakamura, Hayato Nishioka, Jun Rekimoto, Kazuki Kawamura, Momoko Shioki, Shinichi Furuya","submitted_at":"2026-06-09T09:28:46Z","abstract_excerpt":"The quality of piano performance depends on nuanced timing, articulation, and dynamic control, but practice feedback is often summary-based and hard to act on. We introduce Profy, a weakly supervised system that learns from take-level labels derived from aggregated listener ratings (expert-labeled vs. amateur-labeled) to produce time-aligned highlights for review during piano practice. We collected synchronized 1 kHz key-motion and audio from 73 pianists and used 1,083 valid takes for modeling and evaluation. The model outputs clip-level predictions together with evidence scores on a shared re"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.10627","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2026-06-09T09:28:46Z","cross_cats_sorted":["cs.LG","cs.SD"],"title_canon_sha256":"b4ec2a4b17143a468d7a082c631efdcf20c2dfeb105195868ba94695e13f8c3a","abstract_canon_sha256":"a38de9de07facab8210dd6bd5a1c8787ce9df3a8884db223c50bf81cd586646a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T01:10:30.529140Z","signature_b64":"RBHQdoH3ETmJB8E1LkmcJTex0K02ZBVVYbirBrDJmw1UsA7mI5ZDvKuUf6NWKufT1VbcRGavtMIXbUAu65DuCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a757e66ae9d105b201ad68e97390db06e934e6964b3070af74775ce3567373cb","last_reissued_at":"2026-06-10T01:10:30.528353Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T01:10:30.528353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Profy: Interpretable Visualization of Expertise-Dependent Motor Skills Toward Supporting Piano Practice","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SD"],"primary_cat":"cs.HC","authors_text":"Fujiki Nakamura, Hayato Nishioka, Jun Rekimoto, Kazuki Kawamura, Momoko Shioki, Shinichi Furuya","submitted_at":"2026-06-09T09:28:46Z","abstract_excerpt":"The quality of piano performance depends on nuanced timing, articulation, and dynamic control, but practice feedback is often summary-based and hard to act on. We introduce Profy, a weakly supervised system that learns from take-level labels derived from aggregated listener ratings (expert-labeled vs. amateur-labeled) to produce time-aligned highlights for review during piano practice. We collected synchronized 1 kHz key-motion and audio from 73 pianists and used 1,083 valid takes for modeling and evaluation. The model outputs clip-level predictions together with evidence scores on a shared re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10627","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.10627/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.10627","created_at":"2026-06-10T01:10:30.528475+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.10627v1","created_at":"2026-06-10T01:10:30.528475+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.10627","created_at":"2026-06-10T01:10:30.528475+00:00"},{"alias_kind":"pith_short_12","alias_value":"U5L6M2XJ2EC3","created_at":"2026-06-10T01:10:30.528475+00:00"},{"alias_kind":"pith_short_16","alias_value":"U5L6M2XJ2EC3EANN","created_at":"2026-06-10T01:10:30.528475+00:00"},{"alias_kind":"pith_short_8","alias_value":"U5L6M2XJ","created_at":"2026-06-10T01:10:30.528475+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U5L6M2XJ2EC3EANNNDUXHEG3A3","json":"https://pith.science/pith/U5L6M2XJ2EC3EANNNDUXHEG3A3.json","graph_json":"https://pith.science/api/pith-number/U5L6M2XJ2EC3EANNNDUXHEG3A3/graph.json","events_json":"https://pith.science/api/pith-number/U5L6M2XJ2EC3EANNNDUXHEG3A3/events.json","paper":"https://pith.science/paper/U5L6M2XJ"},"agent_actions":{"view_html":"https://pith.science/pith/U5L6M2XJ2EC3EANNNDUXHEG3A3","download_json":"https://pith.science/pith/U5L6M2XJ2EC3EANNNDUXHEG3A3.json","view_paper":"https://pith.science/paper/U5L6M2XJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.10627&json=true","fetch_graph":"https://pith.science/api/pith-number/U5L6M2XJ2EC3EANNNDUXHEG3A3/graph.json","fetch_events":"https://pith.science/api/pith-number/U5L6M2XJ2EC3EANNNDUXHEG3A3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U5L6M2XJ2EC3EANNNDUXHEG3A3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U5L6M2XJ2EC3EANNNDUXHEG3A3/action/storage_attestation","attest_author":"https://pith.science/pith/U5L6M2XJ2EC3EANNNDUXHEG3A3/action/author_attestation","sign_citation":"https://pith.science/pith/U5L6M2XJ2EC3EANNNDUXHEG3A3/action/citation_signature","submit_replication":"https://pith.science/pith/U5L6M2XJ2EC3EANNNDUXHEG3A3/action/replication_record"}},"created_at":"2026-06-10T01:10:30.528475+00:00","updated_at":"2026-06-10T01:10:30.528475+00:00"}