{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NQZBHAT3CIQ5YNIBNWMVCJZVD5","short_pith_number":"pith:NQZBHAT3","schema_version":"1.0","canonical_sha256":"6c3213827b1221dc35016d995127351f753729314beeade6ce9b232c1765e347","source":{"kind":"arxiv","id":"2507.06481","version":1},"attestation_state":"computed","paper":{"title":"IMPACT: Industrial Machine Perception via Acoustic Cognitive Transformer","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Changheon Han, Garam Kim, Hoin Jung, Hojun Lee, Hyung Wook Park, Jiho Lee, Martin Byung-Guk Jun, Sucheol Woo, Yun Seok Kang, Yuseop Sim","submitted_at":"2025-07-09T01:57:39Z","abstract_excerpt":"Acoustic signals from industrial machines offer valuable insights for anomaly detection, predictive maintenance, and operational efficiency enhancement. However, existing task-specific, supervised learning methods often scale poorly and fail to generalize across diverse industrial scenarios, whose acoustic characteristics are distinct from general audio. Furthermore, the scarcity of accessible, large-scale datasets and pretrained models tailored for industrial audio impedes community-driven research and benchmarking. To address these challenges, we introduce DINOS (Diverse INdustrial Operation"},"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":"2507.06481","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SD","submitted_at":"2025-07-09T01:57:39Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"d32b3fe31df59674ded2207573312f23d5b12e22dc86f5c1f47d069ec11043ef","abstract_canon_sha256":"cae60badf5e11167fb72495076936435025c9ee290b06294ea4adc2ad81675b2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:34:16.913148Z","signature_b64":"hg/TKHMn/1zSSa6A+BDo5NApAcWFSxKOdOLjNjZs3Ox4MMiShZ+H2CMH1TP4YIgtnMFXahjEWz1fJPT7ODQiAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c3213827b1221dc35016d995127351f753729314beeade6ce9b232c1765e347","last_reissued_at":"2026-07-05T11:34:16.912653Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:34:16.912653Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"IMPACT: Industrial Machine Perception via Acoustic Cognitive Transformer","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Changheon Han, Garam Kim, Hoin Jung, Hojun Lee, Hyung Wook Park, Jiho Lee, Martin Byung-Guk Jun, Sucheol Woo, Yun Seok Kang, Yuseop Sim","submitted_at":"2025-07-09T01:57:39Z","abstract_excerpt":"Acoustic signals from industrial machines offer valuable insights for anomaly detection, predictive maintenance, and operational efficiency enhancement. However, existing task-specific, supervised learning methods often scale poorly and fail to generalize across diverse industrial scenarios, whose acoustic characteristics are distinct from general audio. Furthermore, the scarcity of accessible, large-scale datasets and pretrained models tailored for industrial audio impedes community-driven research and benchmarking. To address these challenges, we introduce DINOS (Diverse INdustrial Operation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.06481","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/2507.06481/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":"2507.06481","created_at":"2026-07-05T11:34:16.912714+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.06481v1","created_at":"2026-07-05T11:34:16.912714+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.06481","created_at":"2026-07-05T11:34:16.912714+00:00"},{"alias_kind":"pith_short_12","alias_value":"NQZBHAT3CIQ5","created_at":"2026-07-05T11:34:16.912714+00:00"},{"alias_kind":"pith_short_16","alias_value":"NQZBHAT3CIQ5YNIB","created_at":"2026-07-05T11:34:16.912714+00:00"},{"alias_kind":"pith_short_8","alias_value":"NQZBHAT3","created_at":"2026-07-05T11:34:16.912714+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/NQZBHAT3CIQ5YNIBNWMVCJZVD5","json":"https://pith.science/pith/NQZBHAT3CIQ5YNIBNWMVCJZVD5.json","graph_json":"https://pith.science/api/pith-number/NQZBHAT3CIQ5YNIBNWMVCJZVD5/graph.json","events_json":"https://pith.science/api/pith-number/NQZBHAT3CIQ5YNIBNWMVCJZVD5/events.json","paper":"https://pith.science/paper/NQZBHAT3"},"agent_actions":{"view_html":"https://pith.science/pith/NQZBHAT3CIQ5YNIBNWMVCJZVD5","download_json":"https://pith.science/pith/NQZBHAT3CIQ5YNIBNWMVCJZVD5.json","view_paper":"https://pith.science/paper/NQZBHAT3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.06481&json=true","fetch_graph":"https://pith.science/api/pith-number/NQZBHAT3CIQ5YNIBNWMVCJZVD5/graph.json","fetch_events":"https://pith.science/api/pith-number/NQZBHAT3CIQ5YNIBNWMVCJZVD5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NQZBHAT3CIQ5YNIBNWMVCJZVD5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NQZBHAT3CIQ5YNIBNWMVCJZVD5/action/storage_attestation","attest_author":"https://pith.science/pith/NQZBHAT3CIQ5YNIBNWMVCJZVD5/action/author_attestation","sign_citation":"https://pith.science/pith/NQZBHAT3CIQ5YNIBNWMVCJZVD5/action/citation_signature","submit_replication":"https://pith.science/pith/NQZBHAT3CIQ5YNIBNWMVCJZVD5/action/replication_record"}},"created_at":"2026-07-05T11:34:16.912714+00:00","updated_at":"2026-07-05T11:34:16.912714+00:00"}