{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:277VNLJMKW5HAPXFUJCY7NFJ7X","short_pith_number":"pith:277VNLJM","canonical_record":{"source":{"id":"2010.10291","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-10-20T14:04:22Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"20213064ed4fa1f6661ffeb1dda05199c064e663953c55f8e8715223bd5b6ac4","abstract_canon_sha256":"ac127a2400467c3ebf63f03abad2f54a97098fcb87913d12f92a65e5d92566f3"},"schema_version":"1.0"},"canonical_sha256":"d7ff56ad2c55ba703ee5a2458fb4a9fdcf811b377890fad774da29c3992bf389","source":{"kind":"arxiv","id":"2010.10291","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.10291","created_at":"2026-07-05T01:44:29Z"},{"alias_kind":"arxiv_version","alias_value":"2010.10291v1","created_at":"2026-07-05T01:44:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.10291","created_at":"2026-07-05T01:44:29Z"},{"alias_kind":"pith_short_12","alias_value":"277VNLJMKW5H","created_at":"2026-07-05T01:44:29Z"},{"alias_kind":"pith_short_16","alias_value":"277VNLJMKW5HAPXF","created_at":"2026-07-05T01:44:29Z"},{"alias_kind":"pith_short_8","alias_value":"277VNLJM","created_at":"2026-07-05T01:44:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:277VNLJMKW5HAPXFUJCY7NFJ7X","target":"record","payload":{"canonical_record":{"source":{"id":"2010.10291","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-10-20T14:04:22Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"20213064ed4fa1f6661ffeb1dda05199c064e663953c55f8e8715223bd5b6ac4","abstract_canon_sha256":"ac127a2400467c3ebf63f03abad2f54a97098fcb87913d12f92a65e5d92566f3"},"schema_version":"1.0"},"canonical_sha256":"d7ff56ad2c55ba703ee5a2458fb4a9fdcf811b377890fad774da29c3992bf389","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:44:29.210697Z","signature_b64":"pm2X8yljoHwhTDjb9RzXVjVhlWy8voLf657kPY3z6EfzmP4OvGdCWTojyyuhpKxlLRswAOLVuFIsgauDT1OMDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7ff56ad2c55ba703ee5a2458fb4a9fdcf811b377890fad774da29c3992bf389","last_reissued_at":"2026-07-05T01:44:29.210334Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:44:29.210334Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.10291","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:44:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kJMOJy41f5iqwSdtlUTT2u2F8WXj+1oDzyEgBLs/3Wvy8bQZA9qsdBRS5EGqfT6ztJ1k3R+WFCsjNwwhevSJBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T22:56:15.819605Z"},"content_sha256":"1619bcba34819aa4a95eba592067c9cd6e21f3c3df24dd51b939708517914306","schema_version":"1.0","event_id":"sha256:1619bcba34819aa4a95eba592067c9cd6e21f3c3df24dd51b939708517914306"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:277VNLJMKW5HAPXFUJCY7NFJ7X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automatic multitrack mixing with a differentiable mixing console of neural audio effects","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Christian J. Steinmetz, Joan Serr\\`a, Jordi Pons, Santiago Pascual","submitted_at":"2020-10-20T14:04:22Z","abstract_excerpt":"Applications of deep learning to automatic multitrack mixing are largely unexplored. This is partly due to the limited available data, coupled with the fact that such data is relatively unstructured and variable. To address these challenges, we propose a domain-inspired model with a strong inductive bias for the mixing task. We achieve this with the application of pre-trained sub-networks and weight sharing, as well as with a sum/difference stereo loss function. The proposed model can be trained with a limited number of examples, is permutation invariant with respect to the input ordering, and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.10291","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/2010.10291/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:44:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cQ+cS06DUwpFg9UIhqhS3VFU5ZoozNwKxHBVQFaTdyUEH7kYVv91+SFoveWqcLKe5vAvlIp4q2a5Hz3xl9QWCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T22:56:15.820589Z"},"content_sha256":"f4aab78adba0ea2efd06a8fb3736e397bf005aae2bfbf0c5b4a11b12990109d0","schema_version":"1.0","event_id":"sha256:f4aab78adba0ea2efd06a8fb3736e397bf005aae2bfbf0c5b4a11b12990109d0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/277VNLJMKW5HAPXFUJCY7NFJ7X/bundle.json","state_url":"https://pith.science/pith/277VNLJMKW5HAPXFUJCY7NFJ7X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/277VNLJMKW5HAPXFUJCY7NFJ7X/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":"2026-08-03T22:56:15Z","links":{"resolver":"https://pith.science/pith/277VNLJMKW5HAPXFUJCY7NFJ7X","bundle":"https://pith.science/pith/277VNLJMKW5HAPXFUJCY7NFJ7X/bundle.json","state":"https://pith.science/pith/277VNLJMKW5HAPXFUJCY7NFJ7X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/277VNLJMKW5HAPXFUJCY7NFJ7X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:277VNLJMKW5HAPXFUJCY7NFJ7X","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":"ac127a2400467c3ebf63f03abad2f54a97098fcb87913d12f92a65e5d92566f3","cross_cats_sorted":["cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-10-20T14:04:22Z","title_canon_sha256":"20213064ed4fa1f6661ffeb1dda05199c064e663953c55f8e8715223bd5b6ac4"},"schema_version":"1.0","source":{"id":"2010.10291","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.10291","created_at":"2026-07-05T01:44:29Z"},{"alias_kind":"arxiv_version","alias_value":"2010.10291v1","created_at":"2026-07-05T01:44:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.10291","created_at":"2026-07-05T01:44:29Z"},{"alias_kind":"pith_short_12","alias_value":"277VNLJMKW5H","created_at":"2026-07-05T01:44:29Z"},{"alias_kind":"pith_short_16","alias_value":"277VNLJMKW5HAPXF","created_at":"2026-07-05T01:44:29Z"},{"alias_kind":"pith_short_8","alias_value":"277VNLJM","created_at":"2026-07-05T01:44:29Z"}],"graph_snapshots":[{"event_id":"sha256:f4aab78adba0ea2efd06a8fb3736e397bf005aae2bfbf0c5b4a11b12990109d0","target":"graph","created_at":"2026-07-05T01:44:29Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2010.10291/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Applications of deep learning to automatic multitrack mixing are largely unexplored. This is partly due to the limited available data, coupled with the fact that such data is relatively unstructured and variable. To address these challenges, we propose a domain-inspired model with a strong inductive bias for the mixing task. We achieve this with the application of pre-trained sub-networks and weight sharing, as well as with a sum/difference stereo loss function. The proposed model can be trained with a limited number of examples, is permutation invariant with respect to the input ordering, and","authors_text":"Christian J. Steinmetz, Joan Serr\\`a, Jordi Pons, Santiago Pascual","cross_cats":["cs.SD"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-10-20T14:04:22Z","title":"Automatic multitrack mixing with a differentiable mixing console of neural audio effects"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.10291","kind":"arxiv","version":1},"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":"sha256:1619bcba34819aa4a95eba592067c9cd6e21f3c3df24dd51b939708517914306","target":"record","created_at":"2026-07-05T01:44:29Z","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":"ac127a2400467c3ebf63f03abad2f54a97098fcb87913d12f92a65e5d92566f3","cross_cats_sorted":["cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-10-20T14:04:22Z","title_canon_sha256":"20213064ed4fa1f6661ffeb1dda05199c064e663953c55f8e8715223bd5b6ac4"},"schema_version":"1.0","source":{"id":"2010.10291","kind":"arxiv","version":1}},"canonical_sha256":"d7ff56ad2c55ba703ee5a2458fb4a9fdcf811b377890fad774da29c3992bf389","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d7ff56ad2c55ba703ee5a2458fb4a9fdcf811b377890fad774da29c3992bf389","first_computed_at":"2026-07-05T01:44:29.210334Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:44:29.210334Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pm2X8yljoHwhTDjb9RzXVjVhlWy8voLf657kPY3z6EfzmP4OvGdCWTojyyuhpKxlLRswAOLVuFIsgauDT1OMDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:44:29.210697Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.10291","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1619bcba34819aa4a95eba592067c9cd6e21f3c3df24dd51b939708517914306","sha256:f4aab78adba0ea2efd06a8fb3736e397bf005aae2bfbf0c5b4a11b12990109d0"],"state_sha256":"9864315ba4a80eaac53ecd637a5bca01a734482e7b8f9a8737809183c56526cc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hgpUcq5za6LJOPSisz1dykSRqroUVAPwYJa95NzU8o6sJ+B+20hVUh4x95ZFROU85Hw/YLDnRov/jfMEajsgBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T22:56:15.840271Z","bundle_sha256":"deae3c4466ac3a25c457a9d372594924bfbaf461a4cc3318885499ca3280613b"}}