{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PL2LHUNOUHLXL44VBIXPI63XFY","short_pith_number":"pith:PL2LHUNO","schema_version":"1.0","canonical_sha256":"7af4b3d1aea1d775f3950a2ef47b772e32172108d42d03f2ada97f092e220b15","source":{"kind":"arxiv","id":"2501.03464","version":2},"attestation_state":"computed","paper":{"title":"LHGNN: Local-Higher Order Graph Neural Networks For Audio Classification and Tagging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.AS"],"primary_cat":"cs.SD","authors_text":"Dan Stowell, Emmanouil Benetos, Huy Phan, Shubhr Singh","submitted_at":"2025-01-07T01:45:39Z","abstract_excerpt":"Transformers have set new benchmarks in audio processing tasks, leveraging self-attention mechanisms to capture complex patterns and dependencies within audio data. However, their focus on pairwise interactions limits their ability to process the higher-order relations essential for identifying distinct audio objects. To address this limitation, this work introduces the Local- Higher Order Graph Neural Network (LHGNN), a graph based model that enhances feature understanding by integrating local neighbourhood information with higher-order data from Fuzzy C-Means clusters, thereby capturing a br"},"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":"2501.03464","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2025-01-07T01:45:39Z","cross_cats_sorted":["cs.AI","eess.AS"],"title_canon_sha256":"2039576d60054ec63b10960c3cd451daec428160eddbbb120cdaa5ec04e6f2f5","abstract_canon_sha256":"f8f7aba4d8d5951763c520f586cc59d5171ea0fa0e8ab1bdd254940c3225de90"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:06:46.752836Z","signature_b64":"KaBzpNaoNN3H6/CAl+ndxxV1VElO/AoIAR7a7DhVdtewUYyKxNevZBioN+5xqoA/omnK2n18EcqmmO0/xTlVAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7af4b3d1aea1d775f3950a2ef47b772e32172108d42d03f2ada97f092e220b15","last_reissued_at":"2026-07-05T10:06:46.752347Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:06:46.752347Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LHGNN: Local-Higher Order Graph Neural Networks For Audio Classification and Tagging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.AS"],"primary_cat":"cs.SD","authors_text":"Dan Stowell, Emmanouil Benetos, Huy Phan, Shubhr Singh","submitted_at":"2025-01-07T01:45:39Z","abstract_excerpt":"Transformers have set new benchmarks in audio processing tasks, leveraging self-attention mechanisms to capture complex patterns and dependencies within audio data. However, their focus on pairwise interactions limits their ability to process the higher-order relations essential for identifying distinct audio objects. To address this limitation, this work introduces the Local- Higher Order Graph Neural Network (LHGNN), a graph based model that enhances feature understanding by integrating local neighbourhood information with higher-order data from Fuzzy C-Means clusters, thereby capturing a br"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.03464","kind":"arxiv","version":2},"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/2501.03464/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":"2501.03464","created_at":"2026-07-05T10:06:46.752404+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.03464v2","created_at":"2026-07-05T10:06:46.752404+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.03464","created_at":"2026-07-05T10:06:46.752404+00:00"},{"alias_kind":"pith_short_12","alias_value":"PL2LHUNOUHLX","created_at":"2026-07-05T10:06:46.752404+00:00"},{"alias_kind":"pith_short_16","alias_value":"PL2LHUNOUHLXL44V","created_at":"2026-07-05T10:06:46.752404+00:00"},{"alias_kind":"pith_short_8","alias_value":"PL2LHUNO","created_at":"2026-07-05T10:06:46.752404+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/PL2LHUNOUHLXL44VBIXPI63XFY","json":"https://pith.science/pith/PL2LHUNOUHLXL44VBIXPI63XFY.json","graph_json":"https://pith.science/api/pith-number/PL2LHUNOUHLXL44VBIXPI63XFY/graph.json","events_json":"https://pith.science/api/pith-number/PL2LHUNOUHLXL44VBIXPI63XFY/events.json","paper":"https://pith.science/paper/PL2LHUNO"},"agent_actions":{"view_html":"https://pith.science/pith/PL2LHUNOUHLXL44VBIXPI63XFY","download_json":"https://pith.science/pith/PL2LHUNOUHLXL44VBIXPI63XFY.json","view_paper":"https://pith.science/paper/PL2LHUNO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.03464&json=true","fetch_graph":"https://pith.science/api/pith-number/PL2LHUNOUHLXL44VBIXPI63XFY/graph.json","fetch_events":"https://pith.science/api/pith-number/PL2LHUNOUHLXL44VBIXPI63XFY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PL2LHUNOUHLXL44VBIXPI63XFY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PL2LHUNOUHLXL44VBIXPI63XFY/action/storage_attestation","attest_author":"https://pith.science/pith/PL2LHUNOUHLXL44VBIXPI63XFY/action/author_attestation","sign_citation":"https://pith.science/pith/PL2LHUNOUHLXL44VBIXPI63XFY/action/citation_signature","submit_replication":"https://pith.science/pith/PL2LHUNOUHLXL44VBIXPI63XFY/action/replication_record"}},"created_at":"2026-07-05T10:06:46.752404+00:00","updated_at":"2026-07-05T10:06:46.752404+00:00"}