{"paper":{"title":"Towards Open World Sound Event Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Sound event detection can now identify unknown events and learn from them incrementally.","cross_cats":["cs.AI"],"primary_cat":"cs.SD","authors_text":"L.H.Son, L.T.Minh, P.H.Hai","submitted_at":"2026-05-05T16:23:06Z","abstract_excerpt":"Sound Event Detection (SED) plays a vital role in audio understanding, with applications in surveillance, smart cities, healthcare, and multimedia indexing. However, conventional SED systems operate under a closed-world assumption, limiting their effectiveness in real-world environments where novel acoustic events frequently emerge. Inspired by the success of open-world learning in computer vision, we introduce the Open-World Sound Event Detection (OW-SED) paradigm, where models must detect known events, identify unseen ones, and incrementally learn from them. To tackle the unique challenges o"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experimental results demonstrate that our method achieves marginally superior performance compared to existing leading techniques in closed-world settings and significantly improves over existing baselines in open-world scenarios.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the 1D Deformable architecture combined with feature disentanglement, one-to-many matching, and diversity loss can effectively handle overlapping and ambiguous events while enabling incremental learning of novel sounds.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Introduces OW-SED paradigm and WOOT transformer framework to detect known sounds, identify unseen events, and incrementally learn in open audio environments.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Sound event detection can now identify unknown events and learn from them incrementally.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"46f874015e0b76ac436e07f9c10044f8d0ac9bbdc684c7b1c431e516be898d9b"},"source":{"id":"2605.03934","kind":"arxiv","version":2},"verdict":{"id":"bbd5c6ef-b79d-432f-a1b7-86602e3d5bc9","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-07T12:34:44.363054Z","strongest_claim":"Experimental results demonstrate that our method achieves marginally superior performance compared to existing leading techniques in closed-world settings and significantly improves over existing baselines in open-world scenarios.","one_line_summary":"Introduces OW-SED paradigm and WOOT transformer framework to detect known sounds, identify unseen events, and incrementally learn in open audio environments.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the 1D Deformable architecture combined with feature disentanglement, one-to-many matching, and diversity loss can effectively handle overlapping and ambiguous events while enabling incremental learning of novel sounds.","pith_extraction_headline":"Sound event detection can now identify unknown events and learn from them incrementally."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.03934/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T12:40:53.397156Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-20T00:01:21.499481Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T14:55:03.873844Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"46514ca0a7f1fadfcbe29db25510a917fe9310f2f2872fed1b7cb99af34efd16"},"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"}