{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:NYENTY2ZKQ2BVJEFXABIA3O7YT","short_pith_number":"pith:NYENTY2Z","canonical_record":{"source":{"id":"1905.06148","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2019-05-15T12:57:57Z","cross_cats_sorted":["cs.LG","cs.SD","eess.SP"],"title_canon_sha256":"86621ee438301ea6f3379650ec2f090f0145a81a5e95f6c43555c3d260303cea","abstract_canon_sha256":"86dd1a9ea697e3fb94d4e7c07af0c92d41f8df8c0ad86fd48272eb0843248433"},"schema_version":"1.0"},"canonical_sha256":"6e08d9e35954341aa485b802806ddfc4ef17495a8d8c7b0257716901898b3598","source":{"kind":"arxiv","id":"1905.06148","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.06148","created_at":"2026-05-17T23:42:48Z"},{"alias_kind":"arxiv_version","alias_value":"1905.06148v2","created_at":"2026-05-17T23:42:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.06148","created_at":"2026-05-17T23:42:48Z"},{"alias_kind":"pith_short_12","alias_value":"NYENTY2ZKQ2B","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"NYENTY2ZKQ2BVJEF","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"NYENTY2Z","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:NYENTY2ZKQ2BVJEFXABIA3O7YT","target":"record","payload":{"canonical_record":{"source":{"id":"1905.06148","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2019-05-15T12:57:57Z","cross_cats_sorted":["cs.LG","cs.SD","eess.SP"],"title_canon_sha256":"86621ee438301ea6f3379650ec2f090f0145a81a5e95f6c43555c3d260303cea","abstract_canon_sha256":"86dd1a9ea697e3fb94d4e7c07af0c92d41f8df8c0ad86fd48272eb0843248433"},"schema_version":"1.0"},"canonical_sha256":"6e08d9e35954341aa485b802806ddfc4ef17495a8d8c7b0257716901898b3598","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:48.109772Z","signature_b64":"DUmsHvNLahabXl2P2fVFn2nw4HnSvlu3JMV2NLIsuYCfTOezJShK9zVnvsbdv3NSfQ2nn25AJ8J+hcpAuVeTAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e08d9e35954341aa485b802806ddfc4ef17495a8d8c7b0257716901898b3598","last_reissued_at":"2026-05-17T23:42:48.109120Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:48.109120Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.06148","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:42:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9qTTKiePf+9KS3YEa4Yg9HqqIlVb8/9+FvWK9rqj9Tc1j/y4ZH1hc+SfxBbpEWF/OZkghMOj5LJNvjO9pfBHDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T17:44:18.837370Z"},"content_sha256":"79435a67160b68438092f7ad8375be136d801efe3222f8c36212f057dc39d915","schema_version":"1.0","event_id":"sha256:79435a67160b68438092f7ad8375be136d801efe3222f8c36212f057dc39d915"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:NYENTY2ZKQ2BVJEFXABIA3O7YT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A general-purpose deep learning approach to model time-varying audio effects","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SD","eess.SP"],"primary_cat":"eess.AS","authors_text":"Emmanouil Benetos, Joshua D. Reiss, Marco A. Mart\\'inez Ram\\'irez","submitted_at":"2019-05-15T12:57:57Z","abstract_excerpt":"Audio processors whose parameters are modified periodically over time are often referred as time-varying or modulation based audio effects. Most existing methods for modeling these type of effect units are often optimized to a very specific circuit and cannot be efficiently generalized to other time-varying effects. Based on convolutional and recurrent neural networks, we propose a deep learning architecture for generic black-box modeling of audio processors with long-term memory. We explore the capabilities of deep neural networks to learn such long temporal dependencies and we show the netwo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.06148","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:42:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Orbo4Px16sBJ7f1sYMslO6CABDMEYfkyVQcPN9ZqO6nu4UT3B0WukUxZOHd8HwHfFPNhWSTZeLgmrIV3yxsaAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T17:44:18.838113Z"},"content_sha256":"b44c02a5007376e5a675113d732ec10fb04766a4a83008451c67a53d646d2954","schema_version":"1.0","event_id":"sha256:b44c02a5007376e5a675113d732ec10fb04766a4a83008451c67a53d646d2954"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NYENTY2ZKQ2BVJEFXABIA3O7YT/bundle.json","state_url":"https://pith.science/pith/NYENTY2ZKQ2BVJEFXABIA3O7YT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NYENTY2ZKQ2BVJEFXABIA3O7YT/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-08T17:44:18Z","links":{"resolver":"https://pith.science/pith/NYENTY2ZKQ2BVJEFXABIA3O7YT","bundle":"https://pith.science/pith/NYENTY2ZKQ2BVJEFXABIA3O7YT/bundle.json","state":"https://pith.science/pith/NYENTY2ZKQ2BVJEFXABIA3O7YT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NYENTY2ZKQ2BVJEFXABIA3O7YT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:NYENTY2ZKQ2BVJEFXABIA3O7YT","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"86dd1a9ea697e3fb94d4e7c07af0c92d41f8df8c0ad86fd48272eb0843248433","cross_cats_sorted":["cs.LG","cs.SD","eess.SP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2019-05-15T12:57:57Z","title_canon_sha256":"86621ee438301ea6f3379650ec2f090f0145a81a5e95f6c43555c3d260303cea"},"schema_version":"1.0","source":{"id":"1905.06148","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.06148","created_at":"2026-05-17T23:42:48Z"},{"alias_kind":"arxiv_version","alias_value":"1905.06148v2","created_at":"2026-05-17T23:42:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.06148","created_at":"2026-05-17T23:42:48Z"},{"alias_kind":"pith_short_12","alias_value":"NYENTY2ZKQ2B","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"NYENTY2ZKQ2BVJEF","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"NYENTY2Z","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:b44c02a5007376e5a675113d732ec10fb04766a4a83008451c67a53d646d2954","target":"graph","created_at":"2026-05-17T23:42:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Audio processors whose parameters are modified periodically over time are often referred as time-varying or modulation based audio effects. Most existing methods for modeling these type of effect units are often optimized to a very specific circuit and cannot be efficiently generalized to other time-varying effects. Based on convolutional and recurrent neural networks, we propose a deep learning architecture for generic black-box modeling of audio processors with long-term memory. We explore the capabilities of deep neural networks to learn such long temporal dependencies and we show the netwo","authors_text":"Emmanouil Benetos, Joshua D. Reiss, Marco A. Mart\\'inez Ram\\'irez","cross_cats":["cs.LG","cs.SD","eess.SP"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2019-05-15T12:57:57Z","title":"A general-purpose deep learning approach to model time-varying audio effects"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.06148","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:79435a67160b68438092f7ad8375be136d801efe3222f8c36212f057dc39d915","target":"record","created_at":"2026-05-17T23:42:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"86dd1a9ea697e3fb94d4e7c07af0c92d41f8df8c0ad86fd48272eb0843248433","cross_cats_sorted":["cs.LG","cs.SD","eess.SP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2019-05-15T12:57:57Z","title_canon_sha256":"86621ee438301ea6f3379650ec2f090f0145a81a5e95f6c43555c3d260303cea"},"schema_version":"1.0","source":{"id":"1905.06148","kind":"arxiv","version":2}},"canonical_sha256":"6e08d9e35954341aa485b802806ddfc4ef17495a8d8c7b0257716901898b3598","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6e08d9e35954341aa485b802806ddfc4ef17495a8d8c7b0257716901898b3598","first_computed_at":"2026-05-17T23:42:48.109120Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:42:48.109120Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DUmsHvNLahabXl2P2fVFn2nw4HnSvlu3JMV2NLIsuYCfTOezJShK9zVnvsbdv3NSfQ2nn25AJ8J+hcpAuVeTAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:42:48.109772Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.06148","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:79435a67160b68438092f7ad8375be136d801efe3222f8c36212f057dc39d915","sha256:b44c02a5007376e5a675113d732ec10fb04766a4a83008451c67a53d646d2954"],"state_sha256":"1d7dcdf452d86283cef3e0720e3d50c3ab2b90e066bd7d0dd28ae1bff2669940"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l0zMILJ8w3wPjZdOkAMcLJutRC5o/h5IstJdl52EIw3uxZNL/TVnZzPPb5R82sUm99ScCvHRdXsSctEACl6WDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T17:44:18.842114Z","bundle_sha256":"b1296d59af6f4cc60f641b43eab24d02a44fc6763b76b54875c5e99ac378b760"}}