{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:2OQYWHH3WBGGYPNHKC34RQGHCJ","short_pith_number":"pith:2OQYWHH3","schema_version":"1.0","canonical_sha256":"d3a18b1cfbb04c6c3da750b7c8c0c7125a941907fa34462cd46254320faac47b","source":{"kind":"arxiv","id":"1806.07506","version":2},"attestation_state":"computed","paper":{"title":"A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","eess.AS","stat.ML"],"primary_cat":"cs.SD","authors_text":"Eduardo Fonseca, Rong Gong, Xavier Serra","submitted_at":"2018-06-19T23:42:54Z","abstract_excerpt":"In the past, Acoustic Scene Classification systems have been based on hand crafting audio features that are input to a classifier. Nowadays, the common trend is to adopt data driven techniques, e.g., deep learning, where audio representations are learned from data. In this paper, we propose a system that consists of a simple fusion of two methods of the aforementioned types: a deep learning approach where log-scaled mel-spectrograms are input to a convolutional neural network, and a feature engineering approach, where a collection of hand-crafted features is input to a gradient boosting machin"},"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":"1806.07506","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2018-06-19T23:42:54Z","cross_cats_sorted":["cs.LG","eess.AS","stat.ML"],"title_canon_sha256":"75b674a439c360aa9da58ad40d49ff5c5950060ded3cce06d2c3cb63484d68be","abstract_canon_sha256":"4e5ba29894ca6d8caf937aad9a6fae1541518469338d4578720855abc31ee103"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:12:08.504792Z","signature_b64":"5uAYpkIks/lGEzzzIQ/y6Iy6NPy9O2bKvq0tKcYC7UBpCwVglT6JmBoeB8yqumv2gbCs7SmrigKONyBskJh/CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d3a18b1cfbb04c6c3da750b7c8c0c7125a941907fa34462cd46254320faac47b","last_reissued_at":"2026-05-18T00:12:08.504310Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:12:08.504310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","eess.AS","stat.ML"],"primary_cat":"cs.SD","authors_text":"Eduardo Fonseca, Rong Gong, Xavier Serra","submitted_at":"2018-06-19T23:42:54Z","abstract_excerpt":"In the past, Acoustic Scene Classification systems have been based on hand crafting audio features that are input to a classifier. Nowadays, the common trend is to adopt data driven techniques, e.g., deep learning, where audio representations are learned from data. In this paper, we propose a system that consists of a simple fusion of two methods of the aforementioned types: a deep learning approach where log-scaled mel-spectrograms are input to a convolutional neural network, and a feature engineering approach, where a collection of hand-crafted features is input to a gradient boosting machin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.07506","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":"1806.07506","created_at":"2026-05-18T00:12:08.504401+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.07506v2","created_at":"2026-05-18T00:12:08.504401+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.07506","created_at":"2026-05-18T00:12:08.504401+00:00"},{"alias_kind":"pith_short_12","alias_value":"2OQYWHH3WBGG","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"2OQYWHH3WBGGYPNH","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"2OQYWHH3","created_at":"2026-05-18T12:32:02.567920+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/2OQYWHH3WBGGYPNHKC34RQGHCJ","json":"https://pith.science/pith/2OQYWHH3WBGGYPNHKC34RQGHCJ.json","graph_json":"https://pith.science/api/pith-number/2OQYWHH3WBGGYPNHKC34RQGHCJ/graph.json","events_json":"https://pith.science/api/pith-number/2OQYWHH3WBGGYPNHKC34RQGHCJ/events.json","paper":"https://pith.science/paper/2OQYWHH3"},"agent_actions":{"view_html":"https://pith.science/pith/2OQYWHH3WBGGYPNHKC34RQGHCJ","download_json":"https://pith.science/pith/2OQYWHH3WBGGYPNHKC34RQGHCJ.json","view_paper":"https://pith.science/paper/2OQYWHH3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.07506&json=true","fetch_graph":"https://pith.science/api/pith-number/2OQYWHH3WBGGYPNHKC34RQGHCJ/graph.json","fetch_events":"https://pith.science/api/pith-number/2OQYWHH3WBGGYPNHKC34RQGHCJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2OQYWHH3WBGGYPNHKC34RQGHCJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2OQYWHH3WBGGYPNHKC34RQGHCJ/action/storage_attestation","attest_author":"https://pith.science/pith/2OQYWHH3WBGGYPNHKC34RQGHCJ/action/author_attestation","sign_citation":"https://pith.science/pith/2OQYWHH3WBGGYPNHKC34RQGHCJ/action/citation_signature","submit_replication":"https://pith.science/pith/2OQYWHH3WBGGYPNHKC34RQGHCJ/action/replication_record"}},"created_at":"2026-05-18T00:12:08.504401+00:00","updated_at":"2026-05-18T00:12:08.504401+00:00"}