{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:IOLHUMBETNLJECREM6YB5JKAQ2","short_pith_number":"pith:IOLHUMBE","schema_version":"1.0","canonical_sha256":"43967a30249b56920a2467b01ea54086a66d48f78740099ebf6753f02363c318","source":{"kind":"arxiv","id":"2008.02011","version":2},"attestation_state":"computed","paper":{"title":"Neural Loop Combiner: Neural Network Models for Assessing the Compatibility of Loops","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR","cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Bo-Yu Chen, Jordan B. L. Smith, Yi-Hsuan Yang","submitted_at":"2020-08-05T09:16:50Z","abstract_excerpt":"Music producers who use loops may have access to thousands in loop libraries, but finding ones that are compatible is a time-consuming process; we hope to reduce this burden with automation. State-of-the-art systems for estimating compatibility, such as AutoMashUpper, are mostly rule-based and could be improved on with machine learn-ing. To train a model, we need a large set of loops with ground truth compatibility values. No such dataset exists, so we extract loops from existing music to obtain positive examples of compatible loops, and propose and compare various strategies for choosing nega"},"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":"2008.02011","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2020-08-05T09:16:50Z","cross_cats_sorted":["cs.IR","cs.LG","eess.AS"],"title_canon_sha256":"76def3fe2397a2cfe201f8ce0ce7ed7754884fbd9e170c8f6ae91c9d04c9928f","abstract_canon_sha256":"7bb30da6a1a26a9596defcb6b0d8d3a1713e70d969ae83925e07c850dc85ab3c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:57:52.467951Z","signature_b64":"6PZd75/ouz7EBE8mvlRFDvi/fBPpxJvgV42/j8JJznYd4/dycm1v++e7Xx1o0KUW44hZVPwokHnVXgyaTkLjAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43967a30249b56920a2467b01ea54086a66d48f78740099ebf6753f02363c318","last_reissued_at":"2026-07-05T03:57:52.467378Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:57:52.467378Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Loop Combiner: Neural Network Models for Assessing the Compatibility of Loops","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IR","cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Bo-Yu Chen, Jordan B. L. Smith, Yi-Hsuan Yang","submitted_at":"2020-08-05T09:16:50Z","abstract_excerpt":"Music producers who use loops may have access to thousands in loop libraries, but finding ones that are compatible is a time-consuming process; we hope to reduce this burden with automation. State-of-the-art systems for estimating compatibility, such as AutoMashUpper, are mostly rule-based and could be improved on with machine learn-ing. To train a model, we need a large set of loops with ground truth compatibility values. No such dataset exists, so we extract loops from existing music to obtain positive examples of compatible loops, and propose and compare various strategies for choosing nega"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.02011","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2008.02011/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":"2008.02011","created_at":"2026-07-05T03:57:52.467464+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.02011v2","created_at":"2026-07-05T03:57:52.467464+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.02011","created_at":"2026-07-05T03:57:52.467464+00:00"},{"alias_kind":"pith_short_12","alias_value":"IOLHUMBETNLJ","created_at":"2026-07-05T03:57:52.467464+00:00"},{"alias_kind":"pith_short_16","alias_value":"IOLHUMBETNLJECRE","created_at":"2026-07-05T03:57:52.467464+00:00"},{"alias_kind":"pith_short_8","alias_value":"IOLHUMBE","created_at":"2026-07-05T03:57:52.467464+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.23784","citing_title":"Learning Normal Patterns in Musical Loops","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IOLHUMBETNLJECREM6YB5JKAQ2","json":"https://pith.science/pith/IOLHUMBETNLJECREM6YB5JKAQ2.json","graph_json":"https://pith.science/api/pith-number/IOLHUMBETNLJECREM6YB5JKAQ2/graph.json","events_json":"https://pith.science/api/pith-number/IOLHUMBETNLJECREM6YB5JKAQ2/events.json","paper":"https://pith.science/paper/IOLHUMBE"},"agent_actions":{"view_html":"https://pith.science/pith/IOLHUMBETNLJECREM6YB5JKAQ2","download_json":"https://pith.science/pith/IOLHUMBETNLJECREM6YB5JKAQ2.json","view_paper":"https://pith.science/paper/IOLHUMBE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.02011&json=true","fetch_graph":"https://pith.science/api/pith-number/IOLHUMBETNLJECREM6YB5JKAQ2/graph.json","fetch_events":"https://pith.science/api/pith-number/IOLHUMBETNLJECREM6YB5JKAQ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IOLHUMBETNLJECREM6YB5JKAQ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IOLHUMBETNLJECREM6YB5JKAQ2/action/storage_attestation","attest_author":"https://pith.science/pith/IOLHUMBETNLJECREM6YB5JKAQ2/action/author_attestation","sign_citation":"https://pith.science/pith/IOLHUMBETNLJECREM6YB5JKAQ2/action/citation_signature","submit_replication":"https://pith.science/pith/IOLHUMBETNLJECREM6YB5JKAQ2/action/replication_record"}},"created_at":"2026-07-05T03:57:52.467464+00:00","updated_at":"2026-07-05T03:57:52.467464+00:00"}