{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:MAE7RT2S6J4NNYXY53B3WSQZIL","short_pith_number":"pith:MAE7RT2S","schema_version":"1.0","canonical_sha256":"6009f8cf52f278d6e2f8eec3bb4a1942f721cda871b828b01ad70e170810d853","source":{"kind":"arxiv","id":"1910.12539","version":1},"attestation_state":"computed","paper":{"title":"Virtual Piano using Computer Vision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jaeyoon Kim, Seongjae Kang, Sung-Eui Yoon","submitted_at":"2019-10-28T10:36:30Z","abstract_excerpt":"In this research, Piano performances have been analyzed only based on visual information. Computer vision algorithms, e.g., Hough transform and binary thresholding, have been applied to find where the keyboard and specific keys are located. At the same time, Convolutional Neural Networks(CNNs) has been also utilized to find whether specific keys are pressed or not, and how much intensity the keys are pressed only based on visual information. Especially for detecting intensity, a new method of utilizing spatial, temporal CNNs model is devised. Early fusion technique is especially applied in tem"},"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":"1910.12539","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-10-28T10:36:30Z","cross_cats_sorted":[],"title_canon_sha256":"920dfe16a66c1d407da3ffef4441eae5c269e7b2112a12ed101b5d66f45d0059","abstract_canon_sha256":"86a2a4e29fd32a580bd2e0ee502086888c15ee29b77236937fc66ec40887b9a4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:15:17.525951Z","signature_b64":"PliY1QrIE6OT/h1FbNtcI8VFWn0co7R5qZiw0SJ+Z+WCaP+Cr+2w/HKRzicX0ZhHswK6spphrJpELssrqDybCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6009f8cf52f278d6e2f8eec3bb4a1942f721cda871b828b01ad70e170810d853","last_reissued_at":"2026-07-05T00:15:17.525480Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:15:17.525480Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Virtual Piano using Computer Vision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jaeyoon Kim, Seongjae Kang, Sung-Eui Yoon","submitted_at":"2019-10-28T10:36:30Z","abstract_excerpt":"In this research, Piano performances have been analyzed only based on visual information. Computer vision algorithms, e.g., Hough transform and binary thresholding, have been applied to find where the keyboard and specific keys are located. At the same time, Convolutional Neural Networks(CNNs) has been also utilized to find whether specific keys are pressed or not, and how much intensity the keys are pressed only based on visual information. Especially for detecting intensity, a new method of utilizing spatial, temporal CNNs model is devised. Early fusion technique is especially applied in tem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.12539","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/1910.12539/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":"1910.12539","created_at":"2026-07-05T00:15:17.525538+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.12539v1","created_at":"2026-07-05T00:15:17.525538+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.12539","created_at":"2026-07-05T00:15:17.525538+00:00"},{"alias_kind":"pith_short_12","alias_value":"MAE7RT2S6J4N","created_at":"2026-07-05T00:15:17.525538+00:00"},{"alias_kind":"pith_short_16","alias_value":"MAE7RT2S6J4NNYXY","created_at":"2026-07-05T00:15:17.525538+00:00"},{"alias_kind":"pith_short_8","alias_value":"MAE7RT2S","created_at":"2026-07-05T00:15:17.525538+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.03419","citing_title":"Multi-Task Multi-Frame Visual Piano Transcription","ref_index":27,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MAE7RT2S6J4NNYXY53B3WSQZIL","json":"https://pith.science/pith/MAE7RT2S6J4NNYXY53B3WSQZIL.json","graph_json":"https://pith.science/api/pith-number/MAE7RT2S6J4NNYXY53B3WSQZIL/graph.json","events_json":"https://pith.science/api/pith-number/MAE7RT2S6J4NNYXY53B3WSQZIL/events.json","paper":"https://pith.science/paper/MAE7RT2S"},"agent_actions":{"view_html":"https://pith.science/pith/MAE7RT2S6J4NNYXY53B3WSQZIL","download_json":"https://pith.science/pith/MAE7RT2S6J4NNYXY53B3WSQZIL.json","view_paper":"https://pith.science/paper/MAE7RT2S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.12539&json=true","fetch_graph":"https://pith.science/api/pith-number/MAE7RT2S6J4NNYXY53B3WSQZIL/graph.json","fetch_events":"https://pith.science/api/pith-number/MAE7RT2S6J4NNYXY53B3WSQZIL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MAE7RT2S6J4NNYXY53B3WSQZIL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MAE7RT2S6J4NNYXY53B3WSQZIL/action/storage_attestation","attest_author":"https://pith.science/pith/MAE7RT2S6J4NNYXY53B3WSQZIL/action/author_attestation","sign_citation":"https://pith.science/pith/MAE7RT2S6J4NNYXY53B3WSQZIL/action/citation_signature","submit_replication":"https://pith.science/pith/MAE7RT2S6J4NNYXY53B3WSQZIL/action/replication_record"}},"created_at":"2026-07-05T00:15:17.525538+00:00","updated_at":"2026-07-05T00:15:17.525538+00:00"}