{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZEFEBGAPUDPA75HAAJBYVDMTRS","short_pith_number":"pith:ZEFEBGAP","schema_version":"1.0","canonical_sha256":"c90a40980fa0de0ff4e002438a8d938cbc582c3d0fdd985d13497851de73fd93","source":{"kind":"arxiv","id":"2401.09308","version":1},"attestation_state":"computed","paper":{"title":"Can Synthetic Data Boost the Training of Deep Acoustic Vehicle Counting Networks?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Andre Guntoro, Luca Bondi, Shabnam Ghaffarzadegan, Stefano Damiano, Toon van Waterschoot","submitted_at":"2024-01-17T16:18:49Z","abstract_excerpt":"In the design of traffic monitoring solutions for optimizing the urban mobility infrastructure, acoustic vehicle counting models have received attention due to their cost effectiveness and energy efficiency. Although deep learning has proven effective for visual traffic monitoring, its use has not been thoroughly investigated in the audio domain, likely due to real-world data scarcity. In this work, we propose a novel approach to acoustic vehicle counting by developing: i) a traffic noise simulation framework to synthesize realistic vehicle pass-by events; ii) a strategy to mix synthetic and r"},"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":"2401.09308","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2024-01-17T16:18:49Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"ec65cf7470307cd32e5e70fd6b89a70b74eb4b7a840198aa64b566aa64d14744","abstract_canon_sha256":"0076ba4724d7635dc39833097ba771dbf4e5a4b852d6187eb2ccd6c71905217b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:34:42.071532Z","signature_b64":"FrxQUbGDk3JwkfJB0voRcnz3IJVzhVGFmH8kubFIyfMqCa140GHVKz8giAU3odldk8pHFzLGs73vBs/3nfQfAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c90a40980fa0de0ff4e002438a8d938cbc582c3d0fdd985d13497851de73fd93","last_reissued_at":"2026-07-05T07:34:42.071126Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:34:42.071126Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can Synthetic Data Boost the Training of Deep Acoustic Vehicle Counting Networks?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Andre Guntoro, Luca Bondi, Shabnam Ghaffarzadegan, Stefano Damiano, Toon van Waterschoot","submitted_at":"2024-01-17T16:18:49Z","abstract_excerpt":"In the design of traffic monitoring solutions for optimizing the urban mobility infrastructure, acoustic vehicle counting models have received attention due to their cost effectiveness and energy efficiency. Although deep learning has proven effective for visual traffic monitoring, its use has not been thoroughly investigated in the audio domain, likely due to real-world data scarcity. In this work, we propose a novel approach to acoustic vehicle counting by developing: i) a traffic noise simulation framework to synthesize realistic vehicle pass-by events; ii) a strategy to mix synthetic and r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.09308","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/2401.09308/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":"2401.09308","created_at":"2026-07-05T07:34:42.071190+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.09308v1","created_at":"2026-07-05T07:34:42.071190+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.09308","created_at":"2026-07-05T07:34:42.071190+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZEFEBGAPUDPA","created_at":"2026-07-05T07:34:42.071190+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZEFEBGAPUDPA75HA","created_at":"2026-07-05T07:34:42.071190+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZEFEBGAP","created_at":"2026-07-05T07:34:42.071190+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/ZEFEBGAPUDPA75HAAJBYVDMTRS","json":"https://pith.science/pith/ZEFEBGAPUDPA75HAAJBYVDMTRS.json","graph_json":"https://pith.science/api/pith-number/ZEFEBGAPUDPA75HAAJBYVDMTRS/graph.json","events_json":"https://pith.science/api/pith-number/ZEFEBGAPUDPA75HAAJBYVDMTRS/events.json","paper":"https://pith.science/paper/ZEFEBGAP"},"agent_actions":{"view_html":"https://pith.science/pith/ZEFEBGAPUDPA75HAAJBYVDMTRS","download_json":"https://pith.science/pith/ZEFEBGAPUDPA75HAAJBYVDMTRS.json","view_paper":"https://pith.science/paper/ZEFEBGAP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.09308&json=true","fetch_graph":"https://pith.science/api/pith-number/ZEFEBGAPUDPA75HAAJBYVDMTRS/graph.json","fetch_events":"https://pith.science/api/pith-number/ZEFEBGAPUDPA75HAAJBYVDMTRS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZEFEBGAPUDPA75HAAJBYVDMTRS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZEFEBGAPUDPA75HAAJBYVDMTRS/action/storage_attestation","attest_author":"https://pith.science/pith/ZEFEBGAPUDPA75HAAJBYVDMTRS/action/author_attestation","sign_citation":"https://pith.science/pith/ZEFEBGAPUDPA75HAAJBYVDMTRS/action/citation_signature","submit_replication":"https://pith.science/pith/ZEFEBGAPUDPA75HAAJBYVDMTRS/action/replication_record"}},"created_at":"2026-07-05T07:34:42.071190+00:00","updated_at":"2026-07-05T07:34:42.071190+00:00"}