{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:JHHL577AXPZDZ2BG4VZRARWSLD","short_pith_number":"pith:JHHL577A","schema_version":"1.0","canonical_sha256":"49cebeffe0bbf23ce826e5731046d258fd125b78a1a53ff69be70acbaec33d4d","source":{"kind":"arxiv","id":"1804.10070","version":2},"attestation_state":"computed","paper":{"title":"Adaptive pooling operators for weakly labeled sound event detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS","stat.ML"],"primary_cat":"cs.SD","authors_text":"Brian McFee, Juan Pablo Bello, Justin Salamon","submitted_at":"2018-04-26T14:01:12Z","abstract_excerpt":"Sound event detection (SED) methods are tasked with labeling segments of audio recordings by the presence of active sound sources. SED is typically posed as a supervised machine learning problem, requiring strong annotations for the presence or absence of each sound source at every time instant within the recording. However, strong annotations of this type are both labor- and cost-intensive for human annotators to produce, which limits the practical scalability of SED methods.\n  In this work, we treat SED as a multiple instance learning (MIL) problem, where training labels are static over a sh"},"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":"1804.10070","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2018-04-26T14:01:12Z","cross_cats_sorted":["cs.LG","eess.AS","stat.ML"],"title_canon_sha256":"fb0eded7646e3829cfc822dc10803042f0ba2042fc335bb02be0b7e3d81536aa","abstract_canon_sha256":"b1e68a642ffbc57918dc0b63ae7a16fa2bdb5e062f5655429484064f00a75966"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:25.512738Z","signature_b64":"1jQ0GOrkApr2tGdOak1AjmekQz9gqOaKNfcVx6WJycSm7AoxjU5CHy/HLkn+g7ka2beugHnW7IqO1dhpmDInBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49cebeffe0bbf23ce826e5731046d258fd125b78a1a53ff69be70acbaec33d4d","last_reissued_at":"2026-05-18T00:08:25.512269Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:25.512269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adaptive pooling operators for weakly labeled sound event detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS","stat.ML"],"primary_cat":"cs.SD","authors_text":"Brian McFee, Juan Pablo Bello, Justin Salamon","submitted_at":"2018-04-26T14:01:12Z","abstract_excerpt":"Sound event detection (SED) methods are tasked with labeling segments of audio recordings by the presence of active sound sources. SED is typically posed as a supervised machine learning problem, requiring strong annotations for the presence or absence of each sound source at every time instant within the recording. However, strong annotations of this type are both labor- and cost-intensive for human annotators to produce, which limits the practical scalability of SED methods.\n  In this work, we treat SED as a multiple instance learning (MIL) problem, where training labels are static over a sh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.10070","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":""},"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":"1804.10070","created_at":"2026-05-18T00:08:25.512370+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.10070v2","created_at":"2026-05-18T00:08:25.512370+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.10070","created_at":"2026-05-18T00:08:25.512370+00:00"},{"alias_kind":"pith_short_12","alias_value":"JHHL577AXPZD","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"JHHL577AXPZDZ2BG","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"JHHL577A","created_at":"2026-05-18T12:32:31.084164+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/JHHL577AXPZDZ2BG4VZRARWSLD","json":"https://pith.science/pith/JHHL577AXPZDZ2BG4VZRARWSLD.json","graph_json":"https://pith.science/api/pith-number/JHHL577AXPZDZ2BG4VZRARWSLD/graph.json","events_json":"https://pith.science/api/pith-number/JHHL577AXPZDZ2BG4VZRARWSLD/events.json","paper":"https://pith.science/paper/JHHL577A"},"agent_actions":{"view_html":"https://pith.science/pith/JHHL577AXPZDZ2BG4VZRARWSLD","download_json":"https://pith.science/pith/JHHL577AXPZDZ2BG4VZRARWSLD.json","view_paper":"https://pith.science/paper/JHHL577A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.10070&json=true","fetch_graph":"https://pith.science/api/pith-number/JHHL577AXPZDZ2BG4VZRARWSLD/graph.json","fetch_events":"https://pith.science/api/pith-number/JHHL577AXPZDZ2BG4VZRARWSLD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JHHL577AXPZDZ2BG4VZRARWSLD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JHHL577AXPZDZ2BG4VZRARWSLD/action/storage_attestation","attest_author":"https://pith.science/pith/JHHL577AXPZDZ2BG4VZRARWSLD/action/author_attestation","sign_citation":"https://pith.science/pith/JHHL577AXPZDZ2BG4VZRARWSLD/action/citation_signature","submit_replication":"https://pith.science/pith/JHHL577AXPZDZ2BG4VZRARWSLD/action/replication_record"}},"created_at":"2026-05-18T00:08:25.512370+00:00","updated_at":"2026-05-18T00:08:25.512370+00:00"}