{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5OHBMF6NX3K2ROFQGAF5WQREHB","short_pith_number":"pith:5OHBMF6N","schema_version":"1.0","canonical_sha256":"eb8e1617cdbed5a8b8b0300bdb4224384648ee90950f9f3a10be432ac2bf69c1","source":{"kind":"arxiv","id":"1805.10004","version":2},"attestation_state":"computed","paper":{"title":"Masked Conditional Neural Networks for Environmental Sound Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD","eess.AS","stat.ML"],"primary_cat":"cs.LG","authors_text":"David Chesmore, Fady Medhat, John Robinson","submitted_at":"2018-05-25T07:02:38Z","abstract_excerpt":"The ConditionaL Neural Network (CLNN) exploits the nature of the temporal sequencing of the sound signal represented in a spectrogram, and its variant the Masked ConditionaL Neural Network (MCLNN) induces the network to learn in frequency bands by embedding a filterbank-like sparseness over the network's links using a binary mask. Additionally, the masking automates the exploration of different feature combinations concurrently analogous to handcrafting the optimum combination of features for a recognition task. We have evaluated the MCLNN performance using the Urbansound8k dataset of environm"},"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":"1805.10004","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-25T07:02:38Z","cross_cats_sorted":["cs.SD","eess.AS","stat.ML"],"title_canon_sha256":"115a6aecbb2e9565f378fed0286ad7591024f589df5b6ca93f4eae1a99de86c0","abstract_canon_sha256":"5b5fc07eaabe36106324b7bf8c252c2960f73375d6d61605e93fcaeea853177f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:40.001000Z","signature_b64":"97LgsFl3ZNc93+iU+t5osdISTCH1Anpc2ph40vZvHnZTZA/X5Hw6U/Aae7VPkuAoigpjj1MR2ZcVhsAzgznwDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb8e1617cdbed5a8b8b0300bdb4224384648ee90950f9f3a10be432ac2bf69c1","last_reissued_at":"2026-05-17T23:47:40.000583Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:40.000583Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Masked Conditional Neural Networks for Environmental Sound Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD","eess.AS","stat.ML"],"primary_cat":"cs.LG","authors_text":"David Chesmore, Fady Medhat, John Robinson","submitted_at":"2018-05-25T07:02:38Z","abstract_excerpt":"The ConditionaL Neural Network (CLNN) exploits the nature of the temporal sequencing of the sound signal represented in a spectrogram, and its variant the Masked ConditionaL Neural Network (MCLNN) induces the network to learn in frequency bands by embedding a filterbank-like sparseness over the network's links using a binary mask. Additionally, the masking automates the exploration of different feature combinations concurrently analogous to handcrafting the optimum combination of features for a recognition task. We have evaluated the MCLNN performance using the Urbansound8k dataset of environm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10004","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":"1805.10004","created_at":"2026-05-17T23:47:40.000650+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.10004v2","created_at":"2026-05-17T23:47:40.000650+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10004","created_at":"2026-05-17T23:47:40.000650+00:00"},{"alias_kind":"pith_short_12","alias_value":"5OHBMF6NX3K2","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5OHBMF6NX3K2ROFQ","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5OHBMF6N","created_at":"2026-05-18T12:32:08.215937+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/5OHBMF6NX3K2ROFQGAF5WQREHB","json":"https://pith.science/pith/5OHBMF6NX3K2ROFQGAF5WQREHB.json","graph_json":"https://pith.science/api/pith-number/5OHBMF6NX3K2ROFQGAF5WQREHB/graph.json","events_json":"https://pith.science/api/pith-number/5OHBMF6NX3K2ROFQGAF5WQREHB/events.json","paper":"https://pith.science/paper/5OHBMF6N"},"agent_actions":{"view_html":"https://pith.science/pith/5OHBMF6NX3K2ROFQGAF5WQREHB","download_json":"https://pith.science/pith/5OHBMF6NX3K2ROFQGAF5WQREHB.json","view_paper":"https://pith.science/paper/5OHBMF6N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.10004&json=true","fetch_graph":"https://pith.science/api/pith-number/5OHBMF6NX3K2ROFQGAF5WQREHB/graph.json","fetch_events":"https://pith.science/api/pith-number/5OHBMF6NX3K2ROFQGAF5WQREHB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5OHBMF6NX3K2ROFQGAF5WQREHB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5OHBMF6NX3K2ROFQGAF5WQREHB/action/storage_attestation","attest_author":"https://pith.science/pith/5OHBMF6NX3K2ROFQGAF5WQREHB/action/author_attestation","sign_citation":"https://pith.science/pith/5OHBMF6NX3K2ROFQGAF5WQREHB/action/citation_signature","submit_replication":"https://pith.science/pith/5OHBMF6NX3K2ROFQGAF5WQREHB/action/replication_record"}},"created_at":"2026-05-17T23:47:40.000650+00:00","updated_at":"2026-05-17T23:47:40.000650+00:00"}