{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:WM4POG7ERLDORTLH6WBERBU54R","short_pith_number":"pith:WM4POG7E","schema_version":"1.0","canonical_sha256":"b338f71be48ac6e8cd67f58248869de4447ef428c008e1e6c59168cc6f32d1b8","source":{"kind":"arxiv","id":"2110.10812","version":1},"attestation_state":"computed","paper":{"title":"REAL-M: Towards Speech Separation on Real Mixtures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SD","eess.SP"],"primary_cat":"eess.AS","authors_text":"Cem Subakan, Fran\\c{c}ois Grondin, Mirco Ravanelli, Samuele Cornell","submitted_at":"2021-10-20T22:39:35Z","abstract_excerpt":"In recent years, deep learning based source separation has achieved impressive results. Most studies, however, still evaluate separation models on synthetic datasets, while the performance of state-of-the-art techniques on in-the-wild speech data remains an open question. This paper contributes to fill this gap in two ways. First, we release the REAL-M dataset, a crowd-sourced corpus of real-life mixtures. Secondly, we address the problem of performance evaluation of real-life mixtures, where the ground truth is not available. We bypass this issue by carefully designing a blind Scale-Invariant"},"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":"2110.10812","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2021-10-20T22:39:35Z","cross_cats_sorted":["cs.LG","cs.SD","eess.SP"],"title_canon_sha256":"8f1c36775d18f30d52668a87fa5d660eb432fb9409d93203546fbe3573372cfa","abstract_canon_sha256":"5adb23f8221dfe21e0bfee54fa219811da9cb12b3df1a8ef80ae7b9e98e3ff11"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:24:32.751831Z","signature_b64":"FTCeJMvq/5KoJZyb7SIg39GGTs3HaivDCNTkP7yoqGsdyO5frE2p9rXidYJkG93QDKlJ9ZhqKsfxESRgW3/oAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b338f71be48ac6e8cd67f58248869de4447ef428c008e1e6c59168cc6f32d1b8","last_reissued_at":"2026-07-05T03:24:32.751309Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:24:32.751309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"REAL-M: Towards Speech Separation on Real Mixtures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SD","eess.SP"],"primary_cat":"eess.AS","authors_text":"Cem Subakan, Fran\\c{c}ois Grondin, Mirco Ravanelli, Samuele Cornell","submitted_at":"2021-10-20T22:39:35Z","abstract_excerpt":"In recent years, deep learning based source separation has achieved impressive results. Most studies, however, still evaluate separation models on synthetic datasets, while the performance of state-of-the-art techniques on in-the-wild speech data remains an open question. This paper contributes to fill this gap in two ways. First, we release the REAL-M dataset, a crowd-sourced corpus of real-life mixtures. Secondly, we address the problem of performance evaluation of real-life mixtures, where the ground truth is not available. We bypass this issue by carefully designing a blind Scale-Invariant"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.10812","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/2110.10812/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":"2110.10812","created_at":"2026-07-05T03:24:32.751382+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.10812v1","created_at":"2026-07-05T03:24:32.751382+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.10812","created_at":"2026-07-05T03:24:32.751382+00:00"},{"alias_kind":"pith_short_12","alias_value":"WM4POG7ERLDO","created_at":"2026-07-05T03:24:32.751382+00:00"},{"alias_kind":"pith_short_16","alias_value":"WM4POG7ERLDORTLH","created_at":"2026-07-05T03:24:32.751382+00:00"},{"alias_kind":"pith_short_8","alias_value":"WM4POG7E","created_at":"2026-07-05T03:24:32.751382+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WM4POG7ERLDORTLH6WBERBU54R","json":"https://pith.science/pith/WM4POG7ERLDORTLH6WBERBU54R.json","graph_json":"https://pith.science/api/pith-number/WM4POG7ERLDORTLH6WBERBU54R/graph.json","events_json":"https://pith.science/api/pith-number/WM4POG7ERLDORTLH6WBERBU54R/events.json","paper":"https://pith.science/paper/WM4POG7E"},"agent_actions":{"view_html":"https://pith.science/pith/WM4POG7ERLDORTLH6WBERBU54R","download_json":"https://pith.science/pith/WM4POG7ERLDORTLH6WBERBU54R.json","view_paper":"https://pith.science/paper/WM4POG7E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.10812&json=true","fetch_graph":"https://pith.science/api/pith-number/WM4POG7ERLDORTLH6WBERBU54R/graph.json","fetch_events":"https://pith.science/api/pith-number/WM4POG7ERLDORTLH6WBERBU54R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WM4POG7ERLDORTLH6WBERBU54R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WM4POG7ERLDORTLH6WBERBU54R/action/storage_attestation","attest_author":"https://pith.science/pith/WM4POG7ERLDORTLH6WBERBU54R/action/author_attestation","sign_citation":"https://pith.science/pith/WM4POG7ERLDORTLH6WBERBU54R/action/citation_signature","submit_replication":"https://pith.science/pith/WM4POG7ERLDORTLH6WBERBU54R/action/replication_record"}},"created_at":"2026-07-05T03:24:32.751382+00:00","updated_at":"2026-07-05T03:24:32.751382+00:00"}