{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:NP5CTRAQHG6CTWKBKYPWWPL5JC","short_pith_number":"pith:NP5CTRAQ","schema_version":"1.0","canonical_sha256":"6bfa29c41039bc29d941561f6b3d7d48801ded558389d4a14a4143b2549a66b2","source":{"kind":"arxiv","id":"1807.09840","version":2},"attestation_state":"computed","paper":{"title":"A multi-device dataset for urban acoustic scene classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Annamaria Mesaros, Toni Heittola, Tuomas Virtanen","submitted_at":"2018-07-25T20:27:55Z","abstract_excerpt":"This paper introduces the acoustic scene classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task, and evaluates the performance of a baseline system in the task. As in previous years of the challenge, the task is defined for classification of short audio samples into one of predefined acoustic scene classes, using a supervised, closed-set classification setup. The newly recorded TUT Urban Acoustic Scenes 2018 dataset consists of ten different acoustic scenes and was recorded in six large European cities, therefore it has a higher acousti"},"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":"1807.09840","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2018-07-25T20:27:55Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"a95f7669a0bc75d24fad27cfaf19bf5cc2b30958878c33c75f03962c2ab043db","abstract_canon_sha256":"bd74b60376e1b05bd41ed959c5571815e17736043ab5e0487e37d06505468d8b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:36.663288Z","signature_b64":"IAmCr+juI40yIjkUH9r/c+pVH3lsCk2PdSQjU7EVrFcbCKNtJWOSLdmuEFXgnTAeh2kt5WXpt53QlC3/KcerCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6bfa29c41039bc29d941561f6b3d7d48801ded558389d4a14a4143b2549a66b2","last_reissued_at":"2026-05-18T00:03:36.662663Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:36.662663Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A multi-device dataset for urban acoustic scene classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Annamaria Mesaros, Toni Heittola, Tuomas Virtanen","submitted_at":"2018-07-25T20:27:55Z","abstract_excerpt":"This paper introduces the acoustic scene classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task, and evaluates the performance of a baseline system in the task. As in previous years of the challenge, the task is defined for classification of short audio samples into one of predefined acoustic scene classes, using a supervised, closed-set classification setup. The newly recorded TUT Urban Acoustic Scenes 2018 dataset consists of ten different acoustic scenes and was recorded in six large European cities, therefore it has a higher acousti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.09840","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":"1807.09840","created_at":"2026-05-18T00:03:36.662764+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.09840v2","created_at":"2026-05-18T00:03:36.662764+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.09840","created_at":"2026-05-18T00:03:36.662764+00:00"},{"alias_kind":"pith_short_12","alias_value":"NP5CTRAQHG6C","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"NP5CTRAQHG6CTWKB","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"NP5CTRAQ","created_at":"2026-05-18T12:32:40.477152+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2504.15214","citing_title":"Histogram-based Parameter-efficient Tuning for Passive and Active Sonar Classification","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2507.08128","citing_title":"Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models","ref_index":85,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NP5CTRAQHG6CTWKBKYPWWPL5JC","json":"https://pith.science/pith/NP5CTRAQHG6CTWKBKYPWWPL5JC.json","graph_json":"https://pith.science/api/pith-number/NP5CTRAQHG6CTWKBKYPWWPL5JC/graph.json","events_json":"https://pith.science/api/pith-number/NP5CTRAQHG6CTWKBKYPWWPL5JC/events.json","paper":"https://pith.science/paper/NP5CTRAQ"},"agent_actions":{"view_html":"https://pith.science/pith/NP5CTRAQHG6CTWKBKYPWWPL5JC","download_json":"https://pith.science/pith/NP5CTRAQHG6CTWKBKYPWWPL5JC.json","view_paper":"https://pith.science/paper/NP5CTRAQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.09840&json=true","fetch_graph":"https://pith.science/api/pith-number/NP5CTRAQHG6CTWKBKYPWWPL5JC/graph.json","fetch_events":"https://pith.science/api/pith-number/NP5CTRAQHG6CTWKBKYPWWPL5JC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NP5CTRAQHG6CTWKBKYPWWPL5JC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NP5CTRAQHG6CTWKBKYPWWPL5JC/action/storage_attestation","attest_author":"https://pith.science/pith/NP5CTRAQHG6CTWKBKYPWWPL5JC/action/author_attestation","sign_citation":"https://pith.science/pith/NP5CTRAQHG6CTWKBKYPWWPL5JC/action/citation_signature","submit_replication":"https://pith.science/pith/NP5CTRAQHG6CTWKBKYPWWPL5JC/action/replication_record"}},"created_at":"2026-05-18T00:03:36.662764+00:00","updated_at":"2026-05-18T00:03:36.662764+00:00"}