{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7T67ZZ4AE54VMXHWF2D4EFYXED","short_pith_number":"pith:7T67ZZ4A","schema_version":"1.0","canonical_sha256":"fcfdfce7802779565cf62e87c2171720db9f456512460be742ec43dcdea76a5b","source":{"kind":"arxiv","id":"2408.04829","version":1},"attestation_state":"computed","paper":{"title":"Hyper Recurrent Neural Network: Condition Mechanisms for Black-box Audio Effect Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Wen-Yi Hsiao, Yen-Tung Yeh, Yi-Hsuan Yang","submitted_at":"2024-08-09T03:00:25Z","abstract_excerpt":"Recurrent neural networks (RNNs) have demonstrated impressive results for virtual analog modeling of audio effects. These networks process time-domain audio signals using a series of matrix multiplication and nonlinear activation functions to emulate the behavior of the target device accurately. To additionally model the effect of the knobs for an RNN-based model, existing approaches integrate control parameters by concatenating them channel-wisely with some intermediate representation of the input signal. While this method is parameter-efficient, there is room to further improve the quality 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":"2408.04829","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2024-08-09T03:00:25Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"3c3d16aeb7fefdb7cb42292d9093a3ad39d44d84a37f0c9d8b2e4292b7e24d1d","abstract_canon_sha256":"969efeb972b9c96aeb6d328a4cc2b5fcc512ca26ce65d11cf9a708da0ee307cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:53:44.693460Z","signature_b64":"FoKHCcGBbDKbEJUNZ+LhDS8qX3XOuGhTiV15q+TCr3kKGLavkB5eW71dFaLWKSptnGcJ4Y09c2WH3nTkXOieAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fcfdfce7802779565cf62e87c2171720db9f456512460be742ec43dcdea76a5b","last_reissued_at":"2026-07-05T08:53:44.692996Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:53:44.692996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hyper Recurrent Neural Network: Condition Mechanisms for Black-box Audio Effect Modeling","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Wen-Yi Hsiao, Yen-Tung Yeh, Yi-Hsuan Yang","submitted_at":"2024-08-09T03:00:25Z","abstract_excerpt":"Recurrent neural networks (RNNs) have demonstrated impressive results for virtual analog modeling of audio effects. These networks process time-domain audio signals using a series of matrix multiplication and nonlinear activation functions to emulate the behavior of the target device accurately. To additionally model the effect of the knobs for an RNN-based model, existing approaches integrate control parameters by concatenating them channel-wisely with some intermediate representation of the input signal. While this method is parameter-efficient, there is room to further improve the quality o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.04829","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/2408.04829/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":"2408.04829","created_at":"2026-07-05T08:53:44.693055+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.04829v1","created_at":"2026-07-05T08:53:44.693055+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.04829","created_at":"2026-07-05T08:53:44.693055+00:00"},{"alias_kind":"pith_short_12","alias_value":"7T67ZZ4AE54V","created_at":"2026-07-05T08:53:44.693055+00:00"},{"alias_kind":"pith_short_16","alias_value":"7T67ZZ4AE54VMXHW","created_at":"2026-07-05T08:53:44.693055+00:00"},{"alias_kind":"pith_short_8","alias_value":"7T67ZZ4A","created_at":"2026-07-05T08:53:44.693055+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.02109","citing_title":"Parametric Neural Amp Modeling with Active Learning","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7T67ZZ4AE54VMXHWF2D4EFYXED","json":"https://pith.science/pith/7T67ZZ4AE54VMXHWF2D4EFYXED.json","graph_json":"https://pith.science/api/pith-number/7T67ZZ4AE54VMXHWF2D4EFYXED/graph.json","events_json":"https://pith.science/api/pith-number/7T67ZZ4AE54VMXHWF2D4EFYXED/events.json","paper":"https://pith.science/paper/7T67ZZ4A"},"agent_actions":{"view_html":"https://pith.science/pith/7T67ZZ4AE54VMXHWF2D4EFYXED","download_json":"https://pith.science/pith/7T67ZZ4AE54VMXHWF2D4EFYXED.json","view_paper":"https://pith.science/paper/7T67ZZ4A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.04829&json=true","fetch_graph":"https://pith.science/api/pith-number/7T67ZZ4AE54VMXHWF2D4EFYXED/graph.json","fetch_events":"https://pith.science/api/pith-number/7T67ZZ4AE54VMXHWF2D4EFYXED/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7T67ZZ4AE54VMXHWF2D4EFYXED/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7T67ZZ4AE54VMXHWF2D4EFYXED/action/storage_attestation","attest_author":"https://pith.science/pith/7T67ZZ4AE54VMXHWF2D4EFYXED/action/author_attestation","sign_citation":"https://pith.science/pith/7T67ZZ4AE54VMXHWF2D4EFYXED/action/citation_signature","submit_replication":"https://pith.science/pith/7T67ZZ4AE54VMXHWF2D4EFYXED/action/replication_record"}},"created_at":"2026-07-05T08:53:44.693055+00:00","updated_at":"2026-07-05T08:53:44.693055+00:00"}