{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:PK5A7PYNHDXAVRDDHMELPBFE6R","short_pith_number":"pith:PK5A7PYN","canonical_record":{"source":{"id":"2605.21141","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2026-05-20T13:17:17Z","cross_cats_sorted":[],"title_canon_sha256":"fbcf1fa2bde4550a8cd0483373c5bf1c0f390728a5db99cc607bfe3b5ef37046","abstract_canon_sha256":"5efa11b102d755115b4b163c4dc1a2c829c4797a2f09cdc49ff5f419d8d7f8da"},"schema_version":"1.0"},"canonical_sha256":"7aba0fbf0d38ee0ac4633b08b784a4f456c2298fa4798eb6fdf63d0d2b096fdf","source":{"kind":"arxiv","id":"2605.21141","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.21141","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"arxiv_version","alias_value":"2605.21141v1","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21141","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"pith_short_12","alias_value":"PK5A7PYNHDXA","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"pith_short_16","alias_value":"PK5A7PYNHDXAVRDD","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"pith_short_8","alias_value":"PK5A7PYN","created_at":"2026-05-21T01:05:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:PK5A7PYNHDXAVRDDHMELPBFE6R","target":"record","payload":{"canonical_record":{"source":{"id":"2605.21141","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2026-05-20T13:17:17Z","cross_cats_sorted":[],"title_canon_sha256":"fbcf1fa2bde4550a8cd0483373c5bf1c0f390728a5db99cc607bfe3b5ef37046","abstract_canon_sha256":"5efa11b102d755115b4b163c4dc1a2c829c4797a2f09cdc49ff5f419d8d7f8da"},"schema_version":"1.0"},"canonical_sha256":"7aba0fbf0d38ee0ac4633b08b784a4f456c2298fa4798eb6fdf63d0d2b096fdf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:05:39.589099Z","signature_b64":"r557CcREixVmmSUo2V6hBasMhhVNmUqrfSNAUe1Ob7fYl8G1LNUfw/Lxu4ICuCE7ZZcpTWx74+q2Dubq3KMACA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7aba0fbf0d38ee0ac4633b08b784a4f456c2298fa4798eb6fdf63d0d2b096fdf","last_reissued_at":"2026-05-21T01:05:39.588224Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:05:39.588224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.21141","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":"2026-05-21T01:05:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"STrhVjCoR69uYrm0K2xKs9HSLAhy7iOMqsKKcC00+7NMz927uTLlSZwucIGmoDx8QoXd/ZsBrom3in8o+bR3CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T10:23:27.135357Z"},"content_sha256":"141c77715da08db3974b53106d14358a227b0220a77973f19370b5e172dde988","schema_version":"1.0","event_id":"sha256:141c77715da08db3974b53106d14358a227b0220a77973f19370b5e172dde988"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:PK5A7PYNHDXAVRDDHMELPBFE6R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Linearly Constrained Deep Beamformer for Multi-Speaker Scenarios","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.AS","authors_text":"Bar Engel, Ilai Zaidel, Ori Engel, Sharon Gannot","submitted_at":"2026-05-20T13:17:17Z","abstract_excerpt":"We propose a deep beamforming framework for enhancing target speaker(s) in multi-speaker environments. A deep neural network (DNN) is trained to estimate beamforming weights directly from noisy multichannel inputs while satisfying linear spatial constraints through an adaptive multi-term loss inspired by the augmented Lagrangian framework. The loss combines signal reconstruction with penalties that enforce a distortionless response toward the target and suppress the interference subspace. The model is further guided by the target relative transfer function (RTF) and the estimated interference "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21141","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/2605.21141/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":"2026-05-21T01:05:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g+lAB3wBze+cRHOxR3g+tHtPQf/tDwinVtnwqueflsDNfCSwUUT7HUaknrB2YdWanHiP+uY3sehkPGX4czUNDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T10:23:27.136077Z"},"content_sha256":"ed5e1b24908039072313402fa4699dd6df194e3ffb02104a1433e12f72ad2311","schema_version":"1.0","event_id":"sha256:ed5e1b24908039072313402fa4699dd6df194e3ffb02104a1433e12f72ad2311"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PK5A7PYNHDXAVRDDHMELPBFE6R/bundle.json","state_url":"https://pith.science/pith/PK5A7PYNHDXAVRDDHMELPBFE6R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PK5A7PYNHDXAVRDDHMELPBFE6R/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":"2026-05-26T10:23:27Z","links":{"resolver":"https://pith.science/pith/PK5A7PYNHDXAVRDDHMELPBFE6R","bundle":"https://pith.science/pith/PK5A7PYNHDXAVRDDHMELPBFE6R/bundle.json","state":"https://pith.science/pith/PK5A7PYNHDXAVRDDHMELPBFE6R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PK5A7PYNHDXAVRDDHMELPBFE6R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PK5A7PYNHDXAVRDDHMELPBFE6R","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":"5efa11b102d755115b4b163c4dc1a2c829c4797a2f09cdc49ff5f419d8d7f8da","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2026-05-20T13:17:17Z","title_canon_sha256":"fbcf1fa2bde4550a8cd0483373c5bf1c0f390728a5db99cc607bfe3b5ef37046"},"schema_version":"1.0","source":{"id":"2605.21141","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.21141","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"arxiv_version","alias_value":"2605.21141v1","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21141","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"pith_short_12","alias_value":"PK5A7PYNHDXA","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"pith_short_16","alias_value":"PK5A7PYNHDXAVRDD","created_at":"2026-05-21T01:05:39Z"},{"alias_kind":"pith_short_8","alias_value":"PK5A7PYN","created_at":"2026-05-21T01:05:39Z"}],"graph_snapshots":[{"event_id":"sha256:ed5e1b24908039072313402fa4699dd6df194e3ffb02104a1433e12f72ad2311","target":"graph","created_at":"2026-05-21T01:05:39Z","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/2605.21141/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a deep beamforming framework for enhancing target speaker(s) in multi-speaker environments. A deep neural network (DNN) is trained to estimate beamforming weights directly from noisy multichannel inputs while satisfying linear spatial constraints through an adaptive multi-term loss inspired by the augmented Lagrangian framework. The loss combines signal reconstruction with penalties that enforce a distortionless response toward the target and suppress the interference subspace. The model is further guided by the target relative transfer function (RTF) and the estimated interference ","authors_text":"Bar Engel, Ilai Zaidel, Ori Engel, Sharon Gannot","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2026-05-20T13:17:17Z","title":"Linearly Constrained Deep Beamformer for Multi-Speaker Scenarios"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21141","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":"sha256:141c77715da08db3974b53106d14358a227b0220a77973f19370b5e172dde988","target":"record","created_at":"2026-05-21T01:05:39Z","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":"5efa11b102d755115b4b163c4dc1a2c829c4797a2f09cdc49ff5f419d8d7f8da","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2026-05-20T13:17:17Z","title_canon_sha256":"fbcf1fa2bde4550a8cd0483373c5bf1c0f390728a5db99cc607bfe3b5ef37046"},"schema_version":"1.0","source":{"id":"2605.21141","kind":"arxiv","version":1}},"canonical_sha256":"7aba0fbf0d38ee0ac4633b08b784a4f456c2298fa4798eb6fdf63d0d2b096fdf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7aba0fbf0d38ee0ac4633b08b784a4f456c2298fa4798eb6fdf63d0d2b096fdf","first_computed_at":"2026-05-21T01:05:39.588224Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-21T01:05:39.588224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"r557CcREixVmmSUo2V6hBasMhhVNmUqrfSNAUe1Ob7fYl8G1LNUfw/Lxu4ICuCE7ZZcpTWx74+q2Dubq3KMACA==","signature_status":"signed_v1","signed_at":"2026-05-21T01:05:39.589099Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.21141","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:141c77715da08db3974b53106d14358a227b0220a77973f19370b5e172dde988","sha256:ed5e1b24908039072313402fa4699dd6df194e3ffb02104a1433e12f72ad2311"],"state_sha256":"e1c4ea24648c2de51f730420439ac64f95f0571ffe8db6ec135ff4df9dd37e21"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SyPmTNPJLNxqugZbF4zqKrZkfFULJ/ZZMnyG3UmH0HCvJsM7a5RBuCTRWBH4kdPvlBnvxwxfhf3PXB0tBNdTDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T10:23:27.139801Z","bundle_sha256":"193b8e81756b693327409958590a1106067d82170e2e6e8baf8ba74e4e101440"}}