{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:TUJCQPP6NNIWSXA7CBHZXT3PPP","short_pith_number":"pith:TUJCQPP6","schema_version":"1.0","canonical_sha256":"9d12283dfe6b51695c1f104f9bcf6f7bd7771bca5949b3a2431c71e46b8c004a","source":{"kind":"arxiv","id":"2106.08507","version":1},"attestation_state":"computed","paper":{"title":"WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Changliang Xu, Kexun Zhang, Yi Ren, Zhou Zhao","submitted_at":"2021-06-16T01:37:34Z","abstract_excerpt":"Audio super-resolution is the task of constructing a high-resolution (HR) audio from a low-resolution (LR) audio by adding the missing band. Previous methods based on convolutional neural networks and mean squared error training objective have relatively low performance, while adversarial generative models are difficult to train and tune. Recently, normalizing flow has attracted a lot of attention for its high performance, simple training and fast inference. In this paper, we propose WSRGlow, a Glow-based waveform generative model to perform audio super-resolution. Specifically, 1) we integrat"},"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":"2106.08507","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2021-06-16T01:37:34Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"25569c77a9c1b6628fdf1ddf3887e819c8b9399f839f6923eff92c111310cdb5","abstract_canon_sha256":"5e1c6e2a269753221e0f64a2afb097647839bffc3698bf6c9d887e6e6e3e6461"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:49:58.709944Z","signature_b64":"oA3VIW1258aVSEpTZbSeuTdCWyMEruPHbygm/AAyce2AWFNKNs8egxPQjIcfGpIyZqyl9rpzchKqAhvzRSqYAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d12283dfe6b51695c1f104f9bcf6f7bd7771bca5949b3a2431c71e46b8c004a","last_reissued_at":"2026-07-05T02:49:58.709359Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:49:58.709359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Changliang Xu, Kexun Zhang, Yi Ren, Zhou Zhao","submitted_at":"2021-06-16T01:37:34Z","abstract_excerpt":"Audio super-resolution is the task of constructing a high-resolution (HR) audio from a low-resolution (LR) audio by adding the missing band. Previous methods based on convolutional neural networks and mean squared error training objective have relatively low performance, while adversarial generative models are difficult to train and tune. Recently, normalizing flow has attracted a lot of attention for its high performance, simple training and fast inference. In this paper, we propose WSRGlow, a Glow-based waveform generative model to perform audio super-resolution. Specifically, 1) we integrat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.08507","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/2106.08507/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":"2106.08507","created_at":"2026-07-05T02:49:58.709436+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.08507v1","created_at":"2026-07-05T02:49:58.709436+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.08507","created_at":"2026-07-05T02:49:58.709436+00:00"},{"alias_kind":"pith_short_12","alias_value":"TUJCQPP6NNIW","created_at":"2026-07-05T02:49:58.709436+00:00"},{"alias_kind":"pith_short_16","alias_value":"TUJCQPP6NNIWSXA7","created_at":"2026-07-05T02:49:58.709436+00:00"},{"alias_kind":"pith_short_8","alias_value":"TUJCQPP6","created_at":"2026-07-05T02:49:58.709436+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":70,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TUJCQPP6NNIWSXA7CBHZXT3PPP","json":"https://pith.science/pith/TUJCQPP6NNIWSXA7CBHZXT3PPP.json","graph_json":"https://pith.science/api/pith-number/TUJCQPP6NNIWSXA7CBHZXT3PPP/graph.json","events_json":"https://pith.science/api/pith-number/TUJCQPP6NNIWSXA7CBHZXT3PPP/events.json","paper":"https://pith.science/paper/TUJCQPP6"},"agent_actions":{"view_html":"https://pith.science/pith/TUJCQPP6NNIWSXA7CBHZXT3PPP","download_json":"https://pith.science/pith/TUJCQPP6NNIWSXA7CBHZXT3PPP.json","view_paper":"https://pith.science/paper/TUJCQPP6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.08507&json=true","fetch_graph":"https://pith.science/api/pith-number/TUJCQPP6NNIWSXA7CBHZXT3PPP/graph.json","fetch_events":"https://pith.science/api/pith-number/TUJCQPP6NNIWSXA7CBHZXT3PPP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TUJCQPP6NNIWSXA7CBHZXT3PPP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TUJCQPP6NNIWSXA7CBHZXT3PPP/action/storage_attestation","attest_author":"https://pith.science/pith/TUJCQPP6NNIWSXA7CBHZXT3PPP/action/author_attestation","sign_citation":"https://pith.science/pith/TUJCQPP6NNIWSXA7CBHZXT3PPP/action/citation_signature","submit_replication":"https://pith.science/pith/TUJCQPP6NNIWSXA7CBHZXT3PPP/action/replication_record"}},"created_at":"2026-07-05T02:49:58.709436+00:00","updated_at":"2026-07-05T02:49:58.709436+00:00"}