{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:3NBWUPTPF23PSWQSGABMIZYK4E","short_pith_number":"pith:3NBWUPTP","schema_version":"1.0","canonical_sha256":"db436a3e6f2eb6f95a123002c4670ae1373b4daedf40cf815115a7845f5cfb00","source":{"kind":"arxiv","id":"2005.11611","version":3},"attestation_state":"computed","paper":{"title":"Exploring the Best Loss Function for DNN-Based Low-latency Speech Enhancement with Temporal Convolutional Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Bhiksha Raj, Stefan Uhlich, Tyler Vuong, Yuichiro Koyama","submitted_at":"2020-05-23T22:17:49Z","abstract_excerpt":"Recently, deep neural networks (DNNs) have been successfully used for speech enhancement, and DNN-based speech enhancement is becoming an attractive research area. While time-frequency masking based on the short-time Fourier transform (STFT) has been widely used for DNN-based speech enhancement over the last years, time domain methods such as the time-domain audio separation network (TasNet) have also been proposed. The most suitable method depends on the scale of the dataset and the type of task. In this paper, we explore the best speech enhancement algorithm on two different datasets. We pro"},"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":"2005.11611","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-05-23T22:17:49Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"a464651eeb4a4137076df5ba24277cdf67b8bbdd35ba215ee676325315fc2d3f","abstract_canon_sha256":"c82b57e5141943f03ccea138a84e1f13a249a44639cdb24f29313459f912404c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:28:30.640044Z","signature_b64":"ZIqjSF6aeg8tKucNWT4GuUlx/XNuOKS1dwTj+0UrPHeMAH8siXSxx4V6dDGT+vN6Y43qfZMWs/LEBBLIU7VoDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db436a3e6f2eb6f95a123002c4670ae1373b4daedf40cf815115a7845f5cfb00","last_reissued_at":"2026-07-05T01:28:30.639642Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:28:30.639642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring the Best Loss Function for DNN-Based Low-latency Speech Enhancement with Temporal Convolutional Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Bhiksha Raj, Stefan Uhlich, Tyler Vuong, Yuichiro Koyama","submitted_at":"2020-05-23T22:17:49Z","abstract_excerpt":"Recently, deep neural networks (DNNs) have been successfully used for speech enhancement, and DNN-based speech enhancement is becoming an attractive research area. While time-frequency masking based on the short-time Fourier transform (STFT) has been widely used for DNN-based speech enhancement over the last years, time domain methods such as the time-domain audio separation network (TasNet) have also been proposed. The most suitable method depends on the scale of the dataset and the type of task. In this paper, we explore the best speech enhancement algorithm on two different datasets. We pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.11611","kind":"arxiv","version":3},"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/2005.11611/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":"2005.11611","created_at":"2026-07-05T01:28:30.639697+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.11611v3","created_at":"2026-07-05T01:28:30.639697+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.11611","created_at":"2026-07-05T01:28:30.639697+00:00"},{"alias_kind":"pith_short_12","alias_value":"3NBWUPTPF23P","created_at":"2026-07-05T01:28:30.639697+00:00"},{"alias_kind":"pith_short_16","alias_value":"3NBWUPTPF23PSWQS","created_at":"2026-07-05T01:28:30.639697+00:00"},{"alias_kind":"pith_short_8","alias_value":"3NBWUPTP","created_at":"2026-07-05T01:28:30.639697+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/3NBWUPTPF23PSWQSGABMIZYK4E","json":"https://pith.science/pith/3NBWUPTPF23PSWQSGABMIZYK4E.json","graph_json":"https://pith.science/api/pith-number/3NBWUPTPF23PSWQSGABMIZYK4E/graph.json","events_json":"https://pith.science/api/pith-number/3NBWUPTPF23PSWQSGABMIZYK4E/events.json","paper":"https://pith.science/paper/3NBWUPTP"},"agent_actions":{"view_html":"https://pith.science/pith/3NBWUPTPF23PSWQSGABMIZYK4E","download_json":"https://pith.science/pith/3NBWUPTPF23PSWQSGABMIZYK4E.json","view_paper":"https://pith.science/paper/3NBWUPTP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.11611&json=true","fetch_graph":"https://pith.science/api/pith-number/3NBWUPTPF23PSWQSGABMIZYK4E/graph.json","fetch_events":"https://pith.science/api/pith-number/3NBWUPTPF23PSWQSGABMIZYK4E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3NBWUPTPF23PSWQSGABMIZYK4E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3NBWUPTPF23PSWQSGABMIZYK4E/action/storage_attestation","attest_author":"https://pith.science/pith/3NBWUPTPF23PSWQSGABMIZYK4E/action/author_attestation","sign_citation":"https://pith.science/pith/3NBWUPTPF23PSWQSGABMIZYK4E/action/citation_signature","submit_replication":"https://pith.science/pith/3NBWUPTPF23PSWQSGABMIZYK4E/action/replication_record"}},"created_at":"2026-07-05T01:28:30.639697+00:00","updated_at":"2026-07-05T01:28:30.639697+00:00"}