{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:6BRVMRUAPRNGFS2LE7RVY5LA5M","short_pith_number":"pith:6BRVMRUA","canonical_record":{"source":{"id":"2201.05782","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2022-01-15T07:45:10Z","cross_cats_sorted":["cs.IR","cs.LG","cs.MM","eess.AS"],"title_canon_sha256":"9f96da254c18f32482dab2af0504afa017bbd4a68d219082dd4982302d04c4cc","abstract_canon_sha256":"df451680dba02d7accd22fa78871a7277936b8f27d614b366131a82de9f4ae94"},"schema_version":"1.0"},"canonical_sha256":"f0635646807c5a62cb4b27e35c7560eb39ebdf01d4f38c5adbc6678f840bdbf8","source":{"kind":"arxiv","id":"2201.05782","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.05782","created_at":"2026-07-05T03:48:51Z"},{"alias_kind":"arxiv_version","alias_value":"2201.05782v1","created_at":"2026-07-05T03:48:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.05782","created_at":"2026-07-05T03:48:51Z"},{"alias_kind":"pith_short_12","alias_value":"6BRVMRUAPRNG","created_at":"2026-07-05T03:48:51Z"},{"alias_kind":"pith_short_16","alias_value":"6BRVMRUAPRNGFS2L","created_at":"2026-07-05T03:48:51Z"},{"alias_kind":"pith_short_8","alias_value":"6BRVMRUA","created_at":"2026-07-05T03:48:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:6BRVMRUAPRNGFS2LE7RVY5LA5M","target":"record","payload":{"canonical_record":{"source":{"id":"2201.05782","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2022-01-15T07:45:10Z","cross_cats_sorted":["cs.IR","cs.LG","cs.MM","eess.AS"],"title_canon_sha256":"9f96da254c18f32482dab2af0504afa017bbd4a68d219082dd4982302d04c4cc","abstract_canon_sha256":"df451680dba02d7accd22fa78871a7277936b8f27d614b366131a82de9f4ae94"},"schema_version":"1.0"},"canonical_sha256":"f0635646807c5a62cb4b27e35c7560eb39ebdf01d4f38c5adbc6678f840bdbf8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:48:51.517424Z","signature_b64":"+qUVmtUH2lP1ZezFNU2w7UgapnDuOK1G/QJbe3QR/TPl3h80c6RGpk//RP5aqIyu7zeTDm6H8ax1gK4KFq+RDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0635646807c5a62cb4b27e35c7560eb39ebdf01d4f38c5adbc6678f840bdbf8","last_reissued_at":"2026-07-05T03:48:51.517004Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:48:51.517004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2201.05782","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:48:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DdCQ+Hm1QeejWJCSbijqoYJaoXS7sVS8RKITUVOSod2fPjD1P79T96luHYxLdiX96Nkl/7aNXaq3PfZnd/MVDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T21:52:18.481362Z"},"content_sha256":"911ff21dcc93be12218d9ca0cd935048964147c0c937662a0b20e265dd07df69","schema_version":"1.0","event_id":"sha256:911ff21dcc93be12218d9ca0cd935048964147c0c937662a0b20e265dd07df69"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:6BRVMRUAPRNGFS2LE7RVY5LA5M","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG","cs.MM","eess.AS"],"primary_cat":"cs.SD","authors_text":"C. L. Philip Chen, Jibao Qiu, Tong Zhang","submitted_at":"2022-01-15T07:45:10Z","abstract_excerpt":"Symbolic Music Emotion Recognition(SMER) is to predict music emotion from symbolic data, such as MIDI and MusicXML. Previous work mainly focused on learning better representation via (mask) language model pre-training but ignored the intrinsic structure of the music, which is extremely important to the emotional expression of music. In this paper, we present a simple multi-task framework for SMER, which incorporates the emotion recognition task with other emotion-related auxiliary tasks derived from the intrinsic structure of the music. The results show that our multi-task framework can be ada"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.05782","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/2201.05782/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:48:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1o8dis/b1B0tn4XOoX/cLwQtGJ9HNalqaY0bRgdQarq+Sftk3XtjItTvsn1qIwWzAYk0Mt2HlOjaHKx6Sj7AAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T21:52:18.482532Z"},"content_sha256":"42ca1dc974437550754dc4d266d50f7e891f920d5b8a635cab15ec18e58e35aa","schema_version":"1.0","event_id":"sha256:42ca1dc974437550754dc4d266d50f7e891f920d5b8a635cab15ec18e58e35aa"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6BRVMRUAPRNGFS2LE7RVY5LA5M/bundle.json","state_url":"https://pith.science/pith/6BRVMRUAPRNGFS2LE7RVY5LA5M/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6BRVMRUAPRNGFS2LE7RVY5LA5M/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-09T21:52:18Z","links":{"resolver":"https://pith.science/pith/6BRVMRUAPRNGFS2LE7RVY5LA5M","bundle":"https://pith.science/pith/6BRVMRUAPRNGFS2LE7RVY5LA5M/bundle.json","state":"https://pith.science/pith/6BRVMRUAPRNGFS2LE7RVY5LA5M/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6BRVMRUAPRNGFS2LE7RVY5LA5M/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:6BRVMRUAPRNGFS2LE7RVY5LA5M","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"df451680dba02d7accd22fa78871a7277936b8f27d614b366131a82de9f4ae94","cross_cats_sorted":["cs.IR","cs.LG","cs.MM","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2022-01-15T07:45:10Z","title_canon_sha256":"9f96da254c18f32482dab2af0504afa017bbd4a68d219082dd4982302d04c4cc"},"schema_version":"1.0","source":{"id":"2201.05782","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.05782","created_at":"2026-07-05T03:48:51Z"},{"alias_kind":"arxiv_version","alias_value":"2201.05782v1","created_at":"2026-07-05T03:48:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.05782","created_at":"2026-07-05T03:48:51Z"},{"alias_kind":"pith_short_12","alias_value":"6BRVMRUAPRNG","created_at":"2026-07-05T03:48:51Z"},{"alias_kind":"pith_short_16","alias_value":"6BRVMRUAPRNGFS2L","created_at":"2026-07-05T03:48:51Z"},{"alias_kind":"pith_short_8","alias_value":"6BRVMRUA","created_at":"2026-07-05T03:48:51Z"}],"graph_snapshots":[{"event_id":"sha256:42ca1dc974437550754dc4d266d50f7e891f920d5b8a635cab15ec18e58e35aa","target":"graph","created_at":"2026-07-05T03:48:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2201.05782/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Symbolic Music Emotion Recognition(SMER) is to predict music emotion from symbolic data, such as MIDI and MusicXML. Previous work mainly focused on learning better representation via (mask) language model pre-training but ignored the intrinsic structure of the music, which is extremely important to the emotional expression of music. In this paper, we present a simple multi-task framework for SMER, which incorporates the emotion recognition task with other emotion-related auxiliary tasks derived from the intrinsic structure of the music. The results show that our multi-task framework can be ada","authors_text":"C. L. Philip Chen, Jibao Qiu, Tong Zhang","cross_cats":["cs.IR","cs.LG","cs.MM","eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2022-01-15T07:45:10Z","title":"A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.05782","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:911ff21dcc93be12218d9ca0cd935048964147c0c937662a0b20e265dd07df69","target":"record","created_at":"2026-07-05T03:48:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"df451680dba02d7accd22fa78871a7277936b8f27d614b366131a82de9f4ae94","cross_cats_sorted":["cs.IR","cs.LG","cs.MM","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2022-01-15T07:45:10Z","title_canon_sha256":"9f96da254c18f32482dab2af0504afa017bbd4a68d219082dd4982302d04c4cc"},"schema_version":"1.0","source":{"id":"2201.05782","kind":"arxiv","version":1}},"canonical_sha256":"f0635646807c5a62cb4b27e35c7560eb39ebdf01d4f38c5adbc6678f840bdbf8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f0635646807c5a62cb4b27e35c7560eb39ebdf01d4f38c5adbc6678f840bdbf8","first_computed_at":"2026-07-05T03:48:51.517004Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:48:51.517004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+qUVmtUH2lP1ZezFNU2w7UgapnDuOK1G/QJbe3QR/TPl3h80c6RGpk//RP5aqIyu7zeTDm6H8ax1gK4KFq+RDg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:48:51.517424Z","signed_message":"canonical_sha256_bytes"},"source_id":"2201.05782","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:911ff21dcc93be12218d9ca0cd935048964147c0c937662a0b20e265dd07df69","sha256:42ca1dc974437550754dc4d266d50f7e891f920d5b8a635cab15ec18e58e35aa"],"state_sha256":"9009dfa3a281cbee0c54ca7970efbdcf577a83c00a03111922784c13aae8db27"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VZO3IWXIQy7uQQS7g3M35F5bLNmmsR0Sbi+GaoKlYnQbUiz5IfGZSPDmlrlaNypt+XGoYb/EY+dSXd8otCzpDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T21:52:18.505701Z","bundle_sha256":"b02c1245aea774ab8ef721b7b1169cfae9148b459319c2b678f0640b521502bc"}}