{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:3QS6H2FUIKJXP7EEPV3UKZNXJM","short_pith_number":"pith:3QS6H2FU","schema_version":"1.0","canonical_sha256":"dc25e3e8b4429377fc847d774565b74b381d9c97f2aab4e8f574879759dfda80","source":{"kind":"arxiv","id":"1802.04208","version":3},"attestation_state":"computed","paper":{"title":"Adversarial Audio Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SD","authors_text":"Chris Donahue, Julian McAuley, Miller Puckette","submitted_at":"2018-02-12T17:50:43Z","abstract_excerpt":"Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales. Generative adversarial networks (GANs) have seen wide success at generating images that are both locally and globally coherent, but they have seen little application to audio generation. In this paper we introduce WaveGAN, a first attempt at applying GANs to unsupervised synthesis of raw-waveform audio. WaveGAN is capable of synthesizing one second slices of audio waveforms with global coherence, suitable for sound effect generation. Our experiment"},"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":"1802.04208","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2018-02-12T17:50:43Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"80cfba8a553bb0100cf038e85a681a9218a982a55d36df48ff61166d2041c14f","abstract_canon_sha256":"2e1258a987a069c5b8e6db5e3e784d7106c271a1ef7ad41974dddd3997b54d7d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:26.339574Z","signature_b64":"TTK/wgBoRhGfu2iz71XLNm818BxAhIhJuivqavzqJ2VRBd+JWbhj/M32pW3wGWCvpvMDDZGb+3k9zsfSbsHMDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc25e3e8b4429377fc847d774565b74b381d9c97f2aab4e8f574879759dfda80","last_reissued_at":"2026-05-17T23:54:26.338959Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:26.338959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Adversarial Audio Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SD","authors_text":"Chris Donahue, Julian McAuley, Miller Puckette","submitted_at":"2018-02-12T17:50:43Z","abstract_excerpt":"Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales. Generative adversarial networks (GANs) have seen wide success at generating images that are both locally and globally coherent, but they have seen little application to audio generation. In this paper we introduce WaveGAN, a first attempt at applying GANs to unsupervised synthesis of raw-waveform audio. WaveGAN is capable of synthesizing one second slices of audio waveforms with global coherence, suitable for sound effect generation. Our experiment"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.04208","kind":"arxiv","version":3},"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":"1802.04208","created_at":"2026-05-17T23:54:26.339071+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.04208v3","created_at":"2026-05-17T23:54:26.339071+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.04208","created_at":"2026-05-17T23:54:26.339071+00:00"},{"alias_kind":"pith_short_12","alias_value":"3QS6H2FUIKJX","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"3QS6H2FUIKJXP7EE","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"3QS6H2FU","created_at":"2026-05-18T12:32:02.567920+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"1907.06286","citing_title":"Autoencoding sensory substitution","ref_index":201,"is_internal_anchor":true},{"citing_arxiv_id":"2507.15970","citing_title":"CIS-BWE: Chaos-Informed Speech Bandwidth Extension","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16681","citing_title":"A Survey of Advancing Audio Super-Resolution and Bandwidth Extension from Discriminative to Generative Models","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"2604.27182","citing_title":"Preserving Temporal Dynamics in Time Series Generation","ref_index":55,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3QS6H2FUIKJXP7EEPV3UKZNXJM","json":"https://pith.science/pith/3QS6H2FUIKJXP7EEPV3UKZNXJM.json","graph_json":"https://pith.science/api/pith-number/3QS6H2FUIKJXP7EEPV3UKZNXJM/graph.json","events_json":"https://pith.science/api/pith-number/3QS6H2FUIKJXP7EEPV3UKZNXJM/events.json","paper":"https://pith.science/paper/3QS6H2FU"},"agent_actions":{"view_html":"https://pith.science/pith/3QS6H2FUIKJXP7EEPV3UKZNXJM","download_json":"https://pith.science/pith/3QS6H2FUIKJXP7EEPV3UKZNXJM.json","view_paper":"https://pith.science/paper/3QS6H2FU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.04208&json=true","fetch_graph":"https://pith.science/api/pith-number/3QS6H2FUIKJXP7EEPV3UKZNXJM/graph.json","fetch_events":"https://pith.science/api/pith-number/3QS6H2FUIKJXP7EEPV3UKZNXJM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3QS6H2FUIKJXP7EEPV3UKZNXJM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3QS6H2FUIKJXP7EEPV3UKZNXJM/action/storage_attestation","attest_author":"https://pith.science/pith/3QS6H2FUIKJXP7EEPV3UKZNXJM/action/author_attestation","sign_citation":"https://pith.science/pith/3QS6H2FUIKJXP7EEPV3UKZNXJM/action/citation_signature","submit_replication":"https://pith.science/pith/3QS6H2FUIKJXP7EEPV3UKZNXJM/action/replication_record"}},"created_at":"2026-05-17T23:54:26.339071+00:00","updated_at":"2026-05-17T23:54:26.339071+00:00"}