{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FPGCOTZQTF3RTCUSZHSTRCVTOA","short_pith_number":"pith:FPGCOTZQ","schema_version":"1.0","canonical_sha256":"2bcc274f309977198a92c9e5388ab37035a4a21435e1f15d003ad1e4b9e5fb09","source":{"kind":"arxiv","id":"2407.10646","version":1},"attestation_state":"computed","paper":{"title":"Towards zero-shot amplifier modeling: One-to-many amplifier modeling via tone embedding control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Jui-Te Wu, Jyh-Shing Roger Jang, Yen-Tung Yeh, Yi-Hsuan Yang, Yuan-Chiao Cheng, Yu-Hsiang Ho, Yu-Hua Chen","submitted_at":"2024-07-15T12:04:56Z","abstract_excerpt":"Replicating analog device circuits through neural audio effect modeling has garnered increasing interest in recent years. Existing work has predominantly focused on a one-to-one emulation strategy, modeling specific devices individually. In this paper, we tackle the less-explored scenario of one-to-many emulation, utilizing conditioning mechanisms to emulate multiple guitar amplifiers through a single neural model. For condition representation, we use contrastive learning to build a tone embedding encoder that extracts style-related features of various amplifiers, leveraging a dataset of compr"},"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":"2407.10646","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2024-07-15T12:04:56Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"b4fa75d4cb81548a3d4148a149f02ab54d520cd2157e734e508f1f02d1f6b6e4","abstract_canon_sha256":"02f96bcd7226c2fb65cae23169013d780b83b78142089b484854da2545e9f931"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:44:02.513811Z","signature_b64":"mRZmDLJtpxsnefuZGFRkd/KW30iMqg3FIOdbedOrnLZXOCm6d4vFFAg7bMV5K5l0vKPZS65XvVImv9vyII41DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2bcc274f309977198a92c9e5388ab37035a4a21435e1f15d003ad1e4b9e5fb09","last_reissued_at":"2026-07-05T08:44:02.513327Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:44:02.513327Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards zero-shot amplifier modeling: One-to-many amplifier modeling via tone embedding control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Jui-Te Wu, Jyh-Shing Roger Jang, Yen-Tung Yeh, Yi-Hsuan Yang, Yuan-Chiao Cheng, Yu-Hsiang Ho, Yu-Hua Chen","submitted_at":"2024-07-15T12:04:56Z","abstract_excerpt":"Replicating analog device circuits through neural audio effect modeling has garnered increasing interest in recent years. Existing work has predominantly focused on a one-to-one emulation strategy, modeling specific devices individually. In this paper, we tackle the less-explored scenario of one-to-many emulation, utilizing conditioning mechanisms to emulate multiple guitar amplifiers through a single neural model. For condition representation, we use contrastive learning to build a tone embedding encoder that extracts style-related features of various amplifiers, leveraging a dataset of compr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.10646","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/2407.10646/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":"2407.10646","created_at":"2026-07-05T08:44:02.513384+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.10646v1","created_at":"2026-07-05T08:44:02.513384+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.10646","created_at":"2026-07-05T08:44:02.513384+00:00"},{"alias_kind":"pith_short_12","alias_value":"FPGCOTZQTF3R","created_at":"2026-07-05T08:44:02.513384+00:00"},{"alias_kind":"pith_short_16","alias_value":"FPGCOTZQTF3RTCUS","created_at":"2026-07-05T08:44:02.513384+00:00"},{"alias_kind":"pith_short_8","alias_value":"FPGCOTZQ","created_at":"2026-07-05T08:44:02.513384+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.14972","citing_title":"Open-Amp: Synthetic Data Framework for Audio Effect Foundation Models","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FPGCOTZQTF3RTCUSZHSTRCVTOA","json":"https://pith.science/pith/FPGCOTZQTF3RTCUSZHSTRCVTOA.json","graph_json":"https://pith.science/api/pith-number/FPGCOTZQTF3RTCUSZHSTRCVTOA/graph.json","events_json":"https://pith.science/api/pith-number/FPGCOTZQTF3RTCUSZHSTRCVTOA/events.json","paper":"https://pith.science/paper/FPGCOTZQ"},"agent_actions":{"view_html":"https://pith.science/pith/FPGCOTZQTF3RTCUSZHSTRCVTOA","download_json":"https://pith.science/pith/FPGCOTZQTF3RTCUSZHSTRCVTOA.json","view_paper":"https://pith.science/paper/FPGCOTZQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.10646&json=true","fetch_graph":"https://pith.science/api/pith-number/FPGCOTZQTF3RTCUSZHSTRCVTOA/graph.json","fetch_events":"https://pith.science/api/pith-number/FPGCOTZQTF3RTCUSZHSTRCVTOA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FPGCOTZQTF3RTCUSZHSTRCVTOA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FPGCOTZQTF3RTCUSZHSTRCVTOA/action/storage_attestation","attest_author":"https://pith.science/pith/FPGCOTZQTF3RTCUSZHSTRCVTOA/action/author_attestation","sign_citation":"https://pith.science/pith/FPGCOTZQTF3RTCUSZHSTRCVTOA/action/citation_signature","submit_replication":"https://pith.science/pith/FPGCOTZQTF3RTCUSZHSTRCVTOA/action/replication_record"}},"created_at":"2026-07-05T08:44:02.513384+00:00","updated_at":"2026-07-05T08:44:02.513384+00:00"}