{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:EI34X672NP4NBPOJC5YUOLVC4Q","short_pith_number":"pith:EI34X672","schema_version":"1.0","canonical_sha256":"2237cbfbfa6bf8d0bdc91771472ea2e412a236aed227b36e7413ebc815db83ae","source":{"kind":"arxiv","id":"2104.02321","version":2},"attestation_state":"computed","paper":{"title":"NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.AS","authors_text":"Junhyeok Lee, Seungu Han","submitted_at":"2021-04-06T06:52:53Z","abstract_excerpt":"In this work, we introduce NU-Wave, the first neural audio upsampling model to produce waveforms of sampling rate 48kHz from coarse 16kHz or 24kHz inputs, while prior works could generate only up to 16kHz. NU-Wave is the first diffusion probabilistic model for audio super-resolution which is engineered based on neural vocoders. NU-Wave generates high-quality audio that achieves high performance in terms of signal-to-noise ratio (SNR), log-spectral distance (LSD), and accuracy of the ABX test. In all cases, NU-Wave outperforms the baseline models despite the substantially smaller model capacity"},"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":"2104.02321","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2021-04-06T06:52:53Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"56a015fbee200945cb99e8970e1f35046585146c0902342fd8677d16c90a33a9","abstract_canon_sha256":"bcb4f794b41acaca9a7a05e9c25c0ad98d63124b90e07df736a79cfd487740bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:14:31.258442Z","signature_b64":"Ct44CxRRa30H5gc0OzJDmksqCjbwRm1QkOrhYxJQRsSnWEhc4sRj1f/+45ll2QlAsaXKFf2IPicvf8sPCgUeCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2237cbfbfa6bf8d0bdc91771472ea2e412a236aed227b36e7413ebc815db83ae","last_reissued_at":"2026-07-05T05:14:31.257929Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:14:31.257929Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.AS","authors_text":"Junhyeok Lee, Seungu Han","submitted_at":"2021-04-06T06:52:53Z","abstract_excerpt":"In this work, we introduce NU-Wave, the first neural audio upsampling model to produce waveforms of sampling rate 48kHz from coarse 16kHz or 24kHz inputs, while prior works could generate only up to 16kHz. NU-Wave is the first diffusion probabilistic model for audio super-resolution which is engineered based on neural vocoders. NU-Wave generates high-quality audio that achieves high performance in terms of signal-to-noise ratio (SNR), log-spectral distance (LSD), and accuracy of the ABX test. In all cases, NU-Wave outperforms the baseline models despite the substantially smaller model capacity"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.02321","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/2104.02321/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":"2104.02321","created_at":"2026-07-05T05:14:31.257988+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.02321v2","created_at":"2026-07-05T05:14:31.257988+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.02321","created_at":"2026-07-05T05:14:31.257988+00:00"},{"alias_kind":"pith_short_12","alias_value":"EI34X672NP4N","created_at":"2026-07-05T05:14:31.257988+00:00"},{"alias_kind":"pith_short_16","alias_value":"EI34X672NP4NBPOJ","created_at":"2026-07-05T05:14:31.257988+00:00"},{"alias_kind":"pith_short_8","alias_value":"EI34X672","created_at":"2026-07-05T05:14:31.257988+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2209.03003","citing_title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","ref_index":40,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EI34X672NP4NBPOJC5YUOLVC4Q","json":"https://pith.science/pith/EI34X672NP4NBPOJC5YUOLVC4Q.json","graph_json":"https://pith.science/api/pith-number/EI34X672NP4NBPOJC5YUOLVC4Q/graph.json","events_json":"https://pith.science/api/pith-number/EI34X672NP4NBPOJC5YUOLVC4Q/events.json","paper":"https://pith.science/paper/EI34X672"},"agent_actions":{"view_html":"https://pith.science/pith/EI34X672NP4NBPOJC5YUOLVC4Q","download_json":"https://pith.science/pith/EI34X672NP4NBPOJC5YUOLVC4Q.json","view_paper":"https://pith.science/paper/EI34X672","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.02321&json=true","fetch_graph":"https://pith.science/api/pith-number/EI34X672NP4NBPOJC5YUOLVC4Q/graph.json","fetch_events":"https://pith.science/api/pith-number/EI34X672NP4NBPOJC5YUOLVC4Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EI34X672NP4NBPOJC5YUOLVC4Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EI34X672NP4NBPOJC5YUOLVC4Q/action/storage_attestation","attest_author":"https://pith.science/pith/EI34X672NP4NBPOJC5YUOLVC4Q/action/author_attestation","sign_citation":"https://pith.science/pith/EI34X672NP4NBPOJC5YUOLVC4Q/action/citation_signature","submit_replication":"https://pith.science/pith/EI34X672NP4NBPOJC5YUOLVC4Q/action/replication_record"}},"created_at":"2026-07-05T05:14:31.257988+00:00","updated_at":"2026-07-05T05:14:31.257988+00:00"}