{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:IQMDLMONYIZCX43BVROUFKL3BZ","short_pith_number":"pith:IQMDLMON","schema_version":"1.0","canonical_sha256":"441835b1cdc2322bf361ac5d42a97b0e5c8681595b351ef99d49894f82995986","source":{"kind":"arxiv","id":"1711.08016","version":1},"attestation_state":"computed","paper":{"title":"Deep Long Short-Term Memory Adaptive Beamforming Networks For Multichannel Robust Speech Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"Hakan Erdogan, John R. Hershey, Shinji Watanabe, Zhong Meng","submitted_at":"2017-11-21T20:03:03Z","abstract_excerpt":"Far-field speech recognition in noisy and reverberant conditions remains a challenging problem despite recent deep learning breakthroughs. This problem is commonly addressed by acquiring a speech signal from multiple microphones and performing beamforming over them. In this paper, we propose to use a recurrent neural network with long short-term memory (LSTM) architecture to adaptively estimate real-time beamforming filter coefficients to cope with non-stationary environmental noise and dynamic nature of source and microphones positions which results in a set of timevarying room impulse respon"},"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":"1711.08016","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2017-11-21T20:03:03Z","cross_cats_sorted":["cs.CL","cs.SD"],"title_canon_sha256":"9e54c7ff9b8dda192b581413a7bb8a3ab4771f78f73fa1f3d23a8c63666088e3","abstract_canon_sha256":"4c5c20ac97434b1cf076bdf33f21a5fe9003fbea1364564c91e6979111afc487"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:19.278342Z","signature_b64":"XnvLvi4mh4qwBT14vXUMAvnDE8trfrHF125sR3T6tRQfTtCmvPYwEwylYRYoMrKEyuotpO2DnfzO08f5xRKpBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"441835b1cdc2322bf361ac5d42a97b0e5c8681595b351ef99d49894f82995986","last_reissued_at":"2026-05-18T00:03:19.277912Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:19.277912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Long Short-Term Memory Adaptive Beamforming Networks For Multichannel Robust Speech Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"Hakan Erdogan, John R. Hershey, Shinji Watanabe, Zhong Meng","submitted_at":"2017-11-21T20:03:03Z","abstract_excerpt":"Far-field speech recognition in noisy and reverberant conditions remains a challenging problem despite recent deep learning breakthroughs. This problem is commonly addressed by acquiring a speech signal from multiple microphones and performing beamforming over them. In this paper, we propose to use a recurrent neural network with long short-term memory (LSTM) architecture to adaptively estimate real-time beamforming filter coefficients to cope with non-stationary environmental noise and dynamic nature of source and microphones positions which results in a set of timevarying room impulse respon"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.08016","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":""},"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":"1711.08016","created_at":"2026-05-18T00:03:19.277977+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.08016v1","created_at":"2026-05-18T00:03:19.277977+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.08016","created_at":"2026-05-18T00:03:19.277977+00:00"},{"alias_kind":"pith_short_12","alias_value":"IQMDLMONYIZC","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_16","alias_value":"IQMDLMONYIZCX43B","created_at":"2026-05-18T12:31:21.493067+00:00"},{"alias_kind":"pith_short_8","alias_value":"IQMDLMON","created_at":"2026-05-18T12:31:21.493067+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/IQMDLMONYIZCX43BVROUFKL3BZ","json":"https://pith.science/pith/IQMDLMONYIZCX43BVROUFKL3BZ.json","graph_json":"https://pith.science/api/pith-number/IQMDLMONYIZCX43BVROUFKL3BZ/graph.json","events_json":"https://pith.science/api/pith-number/IQMDLMONYIZCX43BVROUFKL3BZ/events.json","paper":"https://pith.science/paper/IQMDLMON"},"agent_actions":{"view_html":"https://pith.science/pith/IQMDLMONYIZCX43BVROUFKL3BZ","download_json":"https://pith.science/pith/IQMDLMONYIZCX43BVROUFKL3BZ.json","view_paper":"https://pith.science/paper/IQMDLMON","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.08016&json=true","fetch_graph":"https://pith.science/api/pith-number/IQMDLMONYIZCX43BVROUFKL3BZ/graph.json","fetch_events":"https://pith.science/api/pith-number/IQMDLMONYIZCX43BVROUFKL3BZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IQMDLMONYIZCX43BVROUFKL3BZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IQMDLMONYIZCX43BVROUFKL3BZ/action/storage_attestation","attest_author":"https://pith.science/pith/IQMDLMONYIZCX43BVROUFKL3BZ/action/author_attestation","sign_citation":"https://pith.science/pith/IQMDLMONYIZCX43BVROUFKL3BZ/action/citation_signature","submit_replication":"https://pith.science/pith/IQMDLMONYIZCX43BVROUFKL3BZ/action/replication_record"}},"created_at":"2026-05-18T00:03:19.277977+00:00","updated_at":"2026-05-18T00:03:19.277977+00:00"}