{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:D3IRW2NGC32J2D3VESJW72466O","short_pith_number":"pith:D3IRW2NG","schema_version":"1.0","canonical_sha256":"1ed11b69a616f49d0f7524936feb9ef3b924a5cd5fa08e5be05aeeeb43dab4ba","source":{"kind":"arxiv","id":"2308.09051","version":1},"attestation_state":"computed","paper":{"title":"Refining a Deep Learning-based Formant Tracker using Linear Prediction Methods","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.SD","eess.SP"],"primary_cat":"eess.AS","authors_text":"Dhananjaya Gowda, Paavo Alku, Sudarsana Reddy Kadiri","submitted_at":"2023-08-17T15:32:32Z","abstract_excerpt":"In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven manner by linear prediction (LP)-based methods. As LP-based formant estimation methods, conventional covariance analysis (LP-COV) and the recently proposed quasi-closed phase forward-backward (QCP-FB) analysis are used. In the proposed refinement approach, the contours of the three lowest formants are first predicted by the data-driven DeepFormants tracker, and the predicted formants are replaced frame-wise with local "},"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":"2308.09051","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2023-08-17T15:32:32Z","cross_cats_sorted":["cs.AI","cs.LG","cs.SD","eess.SP"],"title_canon_sha256":"55b74d73a0b32e7caf908096bffd9bc51047c60948f3ac675cf225ffa01c2225","abstract_canon_sha256":"372e4423d7be8d220a261d7db3048ba0a9adda9ce5e26411fdb4bf71a4666f3d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:42:26.538658Z","signature_b64":"G17YPAYmRfcfakCM03LkjVVY2K6itwfffYxX6BZh9kE3qWooZIRIrQ6NGJUz9Fw8hroxOaNPtJTCjM1RsGgHDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ed11b69a616f49d0f7524936feb9ef3b924a5cd5fa08e5be05aeeeb43dab4ba","last_reissued_at":"2026-07-05T06:42:26.538222Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:42:26.538222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Refining a Deep Learning-based Formant Tracker using Linear Prediction Methods","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.SD","eess.SP"],"primary_cat":"eess.AS","authors_text":"Dhananjaya Gowda, Paavo Alku, Sudarsana Reddy Kadiri","submitted_at":"2023-08-17T15:32:32Z","abstract_excerpt":"In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven manner by linear prediction (LP)-based methods. As LP-based formant estimation methods, conventional covariance analysis (LP-COV) and the recently proposed quasi-closed phase forward-backward (QCP-FB) analysis are used. In the proposed refinement approach, the contours of the three lowest formants are first predicted by the data-driven DeepFormants tracker, and the predicted formants are replaced frame-wise with local "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.09051","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/2308.09051/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":"2308.09051","created_at":"2026-07-05T06:42:26.538282+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.09051v1","created_at":"2026-07-05T06:42:26.538282+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.09051","created_at":"2026-07-05T06:42:26.538282+00:00"},{"alias_kind":"pith_short_12","alias_value":"D3IRW2NGC32J","created_at":"2026-07-05T06:42:26.538282+00:00"},{"alias_kind":"pith_short_16","alias_value":"D3IRW2NGC32J2D3V","created_at":"2026-07-05T06:42:26.538282+00:00"},{"alias_kind":"pith_short_8","alias_value":"D3IRW2NG","created_at":"2026-07-05T06:42:26.538282+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/D3IRW2NGC32J2D3VESJW72466O","json":"https://pith.science/pith/D3IRW2NGC32J2D3VESJW72466O.json","graph_json":"https://pith.science/api/pith-number/D3IRW2NGC32J2D3VESJW72466O/graph.json","events_json":"https://pith.science/api/pith-number/D3IRW2NGC32J2D3VESJW72466O/events.json","paper":"https://pith.science/paper/D3IRW2NG"},"agent_actions":{"view_html":"https://pith.science/pith/D3IRW2NGC32J2D3VESJW72466O","download_json":"https://pith.science/pith/D3IRW2NGC32J2D3VESJW72466O.json","view_paper":"https://pith.science/paper/D3IRW2NG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.09051&json=true","fetch_graph":"https://pith.science/api/pith-number/D3IRW2NGC32J2D3VESJW72466O/graph.json","fetch_events":"https://pith.science/api/pith-number/D3IRW2NGC32J2D3VESJW72466O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D3IRW2NGC32J2D3VESJW72466O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D3IRW2NGC32J2D3VESJW72466O/action/storage_attestation","attest_author":"https://pith.science/pith/D3IRW2NGC32J2D3VESJW72466O/action/author_attestation","sign_citation":"https://pith.science/pith/D3IRW2NGC32J2D3VESJW72466O/action/citation_signature","submit_replication":"https://pith.science/pith/D3IRW2NGC32J2D3VESJW72466O/action/replication_record"}},"created_at":"2026-07-05T06:42:26.538282+00:00","updated_at":"2026-07-05T06:42:26.538282+00:00"}