{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:5P2L6NHABSULBCA6LH6D3A3PJK","short_pith_number":"pith:5P2L6NHA","canonical_record":{"source":{"id":"1703.05003","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2017-03-15T08:33:38Z","cross_cats_sorted":[],"title_canon_sha256":"182c469bd611ef82f8b44ad99dd13746605e127e30909087a083fe09abe1e4d4","abstract_canon_sha256":"82d07a8f3966773c6870943c76566b9db68e4aedb070f03c34b8d80a6dd437d9"},"schema_version":"1.0"},"canonical_sha256":"ebf4bf34e00ca8b0881e59fc3d836f4a8f61e377857fcf75c0e5fff8e0d52316","source":{"kind":"arxiv","id":"1703.05003","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.05003","created_at":"2026-05-18T00:26:00Z"},{"alias_kind":"arxiv_version","alias_value":"1703.05003v2","created_at":"2026-05-18T00:26:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.05003","created_at":"2026-05-18T00:26:00Z"},{"alias_kind":"pith_short_12","alias_value":"5P2L6NHABSUL","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"5P2L6NHABSULBCA6","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"5P2L6NHA","created_at":"2026-05-18T12:31:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:5P2L6NHABSULBCA6LH6D3A3PJK","target":"record","payload":{"canonical_record":{"source":{"id":"1703.05003","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2017-03-15T08:33:38Z","cross_cats_sorted":[],"title_canon_sha256":"182c469bd611ef82f8b44ad99dd13746605e127e30909087a083fe09abe1e4d4","abstract_canon_sha256":"82d07a8f3966773c6870943c76566b9db68e4aedb070f03c34b8d80a6dd437d9"},"schema_version":"1.0"},"canonical_sha256":"ebf4bf34e00ca8b0881e59fc3d836f4a8f61e377857fcf75c0e5fff8e0d52316","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:00.693694Z","signature_b64":"/gnIhc3503QFMEty/2pCSs7hz0Ajd6eSvH36scIULjFQja8liE6j0FOQMbgwqG2l+Dezkeq+B1dGKtmi6fq4Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebf4bf34e00ca8b0881e59fc3d836f4a8f61e377857fcf75c0e5fff8e0d52316","last_reissued_at":"2026-05-18T00:26:00.692956Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:00.692956Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.05003","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:26:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mbCbvDayaOhAdbRmfc7+NEm8MUJ1hu7z3cVWn1z4WrIoXqPpD/Rorv0EuKfGB5vgIdyXPJFVbUVW8n+ppc6oCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T21:27:56.551668Z"},"content_sha256":"1b6ff45d36a319a9958e990f821b63c4457e9165f69f183e4124b7e7cab15c3f","schema_version":"1.0","event_id":"sha256:1b6ff45d36a319a9958e990f821b63c4457e9165f69f183e4124b7e7cab15c3f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:5P2L6NHABSULBCA6LH6D3A3PJK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On the Importance of Super-Gaussian Speech Priors for Machine-Learning Based Speech Enhancement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SD","authors_text":"Robert Rehr, Timo Gerkmann","submitted_at":"2017-03-15T08:33:38Z","abstract_excerpt":"For enhancing noisy signals, machine-learning based single-channel speech enhancement schemes exploit prior knowledge about typical speech spectral structures. To ensure a good generalization and to meet requirements in terms of computational complexity and memory consumption, certain methods restrict themselves to learning speech spectral envelopes. We refer to these approaches as machine-learning spectral envelope (MLSE)-based approaches.\n  In this paper we show by means of theoretical and experimental analyses that for MLSE-based approaches, super-Gaussian priors allow for a reduction of no"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.05003","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":""},"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":"2026-05-18T00:26:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mPgRgVf6OsZYis11YuFtOJ5/3tdEuijiz67HV32jr9aJRaP4cgtFb7FpS5ll/6kKhRsRBYfktL+at7e9THIRBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T21:27:56.552369Z"},"content_sha256":"2105327ae9c899440e6544e39b9239dab40023bd85718cd8c7c41d631f12c46a","schema_version":"1.0","event_id":"sha256:2105327ae9c899440e6544e39b9239dab40023bd85718cd8c7c41d631f12c46a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5P2L6NHABSULBCA6LH6D3A3PJK/bundle.json","state_url":"https://pith.science/pith/5P2L6NHABSULBCA6LH6D3A3PJK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5P2L6NHABSULBCA6LH6D3A3PJK/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":"2026-06-11T21:27:56Z","links":{"resolver":"https://pith.science/pith/5P2L6NHABSULBCA6LH6D3A3PJK","bundle":"https://pith.science/pith/5P2L6NHABSULBCA6LH6D3A3PJK/bundle.json","state":"https://pith.science/pith/5P2L6NHABSULBCA6LH6D3A3PJK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5P2L6NHABSULBCA6LH6D3A3PJK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:5P2L6NHABSULBCA6LH6D3A3PJK","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":"82d07a8f3966773c6870943c76566b9db68e4aedb070f03c34b8d80a6dd437d9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2017-03-15T08:33:38Z","title_canon_sha256":"182c469bd611ef82f8b44ad99dd13746605e127e30909087a083fe09abe1e4d4"},"schema_version":"1.0","source":{"id":"1703.05003","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.05003","created_at":"2026-05-18T00:26:00Z"},{"alias_kind":"arxiv_version","alias_value":"1703.05003v2","created_at":"2026-05-18T00:26:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.05003","created_at":"2026-05-18T00:26:00Z"},{"alias_kind":"pith_short_12","alias_value":"5P2L6NHABSUL","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"5P2L6NHABSULBCA6","created_at":"2026-05-18T12:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"5P2L6NHA","created_at":"2026-05-18T12:31:00Z"}],"graph_snapshots":[{"event_id":"sha256:2105327ae9c899440e6544e39b9239dab40023bd85718cd8c7c41d631f12c46a","target":"graph","created_at":"2026-05-18T00:26:00Z","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"},"paper":{"abstract_excerpt":"For enhancing noisy signals, machine-learning based single-channel speech enhancement schemes exploit prior knowledge about typical speech spectral structures. To ensure a good generalization and to meet requirements in terms of computational complexity and memory consumption, certain methods restrict themselves to learning speech spectral envelopes. We refer to these approaches as machine-learning spectral envelope (MLSE)-based approaches.\n  In this paper we show by means of theoretical and experimental analyses that for MLSE-based approaches, super-Gaussian priors allow for a reduction of no","authors_text":"Robert Rehr, Timo Gerkmann","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2017-03-15T08:33:38Z","title":"On the Importance of Super-Gaussian Speech Priors for Machine-Learning Based Speech Enhancement"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.05003","kind":"arxiv","version":2},"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":"sha256:1b6ff45d36a319a9958e990f821b63c4457e9165f69f183e4124b7e7cab15c3f","target":"record","created_at":"2026-05-18T00:26:00Z","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":"82d07a8f3966773c6870943c76566b9db68e4aedb070f03c34b8d80a6dd437d9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2017-03-15T08:33:38Z","title_canon_sha256":"182c469bd611ef82f8b44ad99dd13746605e127e30909087a083fe09abe1e4d4"},"schema_version":"1.0","source":{"id":"1703.05003","kind":"arxiv","version":2}},"canonical_sha256":"ebf4bf34e00ca8b0881e59fc3d836f4a8f61e377857fcf75c0e5fff8e0d52316","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ebf4bf34e00ca8b0881e59fc3d836f4a8f61e377857fcf75c0e5fff8e0d52316","first_computed_at":"2026-05-18T00:26:00.692956Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:26:00.692956Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/gnIhc3503QFMEty/2pCSs7hz0Ajd6eSvH36scIULjFQja8liE6j0FOQMbgwqG2l+Dezkeq+B1dGKtmi6fq4Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:26:00.693694Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.05003","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1b6ff45d36a319a9958e990f821b63c4457e9165f69f183e4124b7e7cab15c3f","sha256:2105327ae9c899440e6544e39b9239dab40023bd85718cd8c7c41d631f12c46a"],"state_sha256":"7e48da12667624c4c824b53d17f7596dfbf3f5c73bff619599ac802ce66ed809"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C7AwFKx/ue1EsZxsaL5AsymyZwzwpTvVyEhYBwYm7DD2gb51OL8WBRFHE/W1T/iQkJh1+0UNldS1qKk3wUHzCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T21:27:56.557063Z","bundle_sha256":"14db460bf6eb9eebc6ecad39f1b3976220a12201c32c9069227777474d7adf52"}}