{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:CJKLWWKPXXRGAVAUZF6WD3AZZU","short_pith_number":"pith:CJKLWWKP","schema_version":"1.0","canonical_sha256":"1254bb594fbde2605414c97d61ec19cd34c18fdae485cae8668d30c7833302d0","source":{"kind":"arxiv","id":"2206.08545","version":2},"attestation_state":"computed","paper":{"title":"NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.AS","authors_text":"Junhyeok Lee, Seungu Han","submitted_at":"2022-06-17T04:40:14Z","abstract_excerpt":"Conventionally, audio super-resolution models fixed the initial and the target sampling rates, which necessitate the model to be trained for each pair of sampling rates. We introduce NU-Wave 2, a diffusion model for neural audio upsampling that enables the generation of 48 kHz audio signals from inputs of various sampling rates with a single model. Based on the architecture of NU-Wave, NU-Wave 2 uses short-time Fourier convolution (STFC) to generate harmonics to resolve the main failure modes of NU-Wave, and incorporates bandwidth spectral feature transform (BSFT) to condition the bandwidths o"},"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":"2206.08545","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2022-06-17T04:40:14Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"977a033560aacfaa737f80a48a4927cbcf031015bfe7ef2f49cd7ee0c297b35d","abstract_canon_sha256":"9e5e68e9c56352a1ca2b22a17bead6228609ee9672d8f2582bb49d824f341370"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:01:02.178142Z","signature_b64":"6vmar4NrDZd7+M7Ozjx7C6MZjDfUHD9vDxP/t+UEUKbQAtqz1LjzxVieQhWjG1JV70QmlQk6TAKtBygISm9qDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1254bb594fbde2605414c97d61ec19cd34c18fdae485cae8668d30c7833302d0","last_reissued_at":"2026-07-05T05:01:02.177728Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:01:02.177728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.AS","authors_text":"Junhyeok Lee, Seungu Han","submitted_at":"2022-06-17T04:40:14Z","abstract_excerpt":"Conventionally, audio super-resolution models fixed the initial and the target sampling rates, which necessitate the model to be trained for each pair of sampling rates. We introduce NU-Wave 2, a diffusion model for neural audio upsampling that enables the generation of 48 kHz audio signals from inputs of various sampling rates with a single model. Based on the architecture of NU-Wave, NU-Wave 2 uses short-time Fourier convolution (STFC) to generate harmonics to resolve the main failure modes of NU-Wave, and incorporates bandwidth spectral feature transform (BSFT) to condition the bandwidths o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.08545","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2206.08545/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":"2206.08545","created_at":"2026-07-05T05:01:02.177782+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.08545v2","created_at":"2026-07-05T05:01:02.177782+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.08545","created_at":"2026-07-05T05:01:02.177782+00:00"},{"alias_kind":"pith_short_12","alias_value":"CJKLWWKPXXRG","created_at":"2026-07-05T05:01:02.177782+00:00"},{"alias_kind":"pith_short_16","alias_value":"CJKLWWKPXXRGAVAU","created_at":"2026-07-05T05:01:02.177782+00:00"},{"alias_kind":"pith_short_8","alias_value":"CJKLWWKP","created_at":"2026-07-05T05:01:02.177782+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.16681","citing_title":"A Survey of Advancing Audio Super-Resolution and Bandwidth Extension from Discriminative to Generative Models","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CJKLWWKPXXRGAVAUZF6WD3AZZU","json":"https://pith.science/pith/CJKLWWKPXXRGAVAUZF6WD3AZZU.json","graph_json":"https://pith.science/api/pith-number/CJKLWWKPXXRGAVAUZF6WD3AZZU/graph.json","events_json":"https://pith.science/api/pith-number/CJKLWWKPXXRGAVAUZF6WD3AZZU/events.json","paper":"https://pith.science/paper/CJKLWWKP"},"agent_actions":{"view_html":"https://pith.science/pith/CJKLWWKPXXRGAVAUZF6WD3AZZU","download_json":"https://pith.science/pith/CJKLWWKPXXRGAVAUZF6WD3AZZU.json","view_paper":"https://pith.science/paper/CJKLWWKP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.08545&json=true","fetch_graph":"https://pith.science/api/pith-number/CJKLWWKPXXRGAVAUZF6WD3AZZU/graph.json","fetch_events":"https://pith.science/api/pith-number/CJKLWWKPXXRGAVAUZF6WD3AZZU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CJKLWWKPXXRGAVAUZF6WD3AZZU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CJKLWWKPXXRGAVAUZF6WD3AZZU/action/storage_attestation","attest_author":"https://pith.science/pith/CJKLWWKPXXRGAVAUZF6WD3AZZU/action/author_attestation","sign_citation":"https://pith.science/pith/CJKLWWKPXXRGAVAUZF6WD3AZZU/action/citation_signature","submit_replication":"https://pith.science/pith/CJKLWWKPXXRGAVAUZF6WD3AZZU/action/replication_record"}},"created_at":"2026-07-05T05:01:02.177782+00:00","updated_at":"2026-07-05T05:01:02.177782+00:00"}