{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:UPFTBOLWHZ3OWWIH2XPHZXBPWN","short_pith_number":"pith:UPFTBOLW","schema_version":"1.0","canonical_sha256":"a3cb30b9763e76eb5907d5de7cdc2fb352f8ffe986689157bc1eea1d08995426","source":{"kind":"arxiv","id":"2101.06707","version":1},"attestation_state":"computed","paper":{"title":"Fourier, Gabor, Morlet or Wigner: Comparison of Time-Frequency Transforms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Stefan Scholl","submitted_at":"2021-01-17T16:39:33Z","abstract_excerpt":"In digital signal processing time-frequency transforms are used to analyze time-varying signals with respect to their spectral contents over time. Apart from the commonly used short-time Fourier transform, other methods exist in literature, such as the Wavelet, Stockwell or Wigner-Ville transform. Consequently, engineers working on digital signal processing tasks are often faced with the question which transform is appropriate for a specific application. To address this question, this paper first briefly introduces the different transforms. Then it compares them with respect to the achievable "},"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":"2101.06707","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2021-01-17T16:39:33Z","cross_cats_sorted":[],"title_canon_sha256":"1c4a87a80fea57fb92dc7c7d29bfd5078365cb2eddef36fc7dde3b771f9674c6","abstract_canon_sha256":"676574745f8dfcedb24b178954fa9275dd541a54fb3e369110360d2e75ae57a0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:07:36.413630Z","signature_b64":"C8/0aFdhUU013TW9QH9d9yKoaH01s4IFWbSQ8rHOnejQk4bj9fRFwbnjkrEXnOBxBeTcQolyl30zHbOwqjuFBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3cb30b9763e76eb5907d5de7cdc2fb352f8ffe986689157bc1eea1d08995426","last_reissued_at":"2026-07-05T02:07:36.413178Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:07:36.413178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fourier, Gabor, Morlet or Wigner: Comparison of Time-Frequency Transforms","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Stefan Scholl","submitted_at":"2021-01-17T16:39:33Z","abstract_excerpt":"In digital signal processing time-frequency transforms are used to analyze time-varying signals with respect to their spectral contents over time. Apart from the commonly used short-time Fourier transform, other methods exist in literature, such as the Wavelet, Stockwell or Wigner-Ville transform. Consequently, engineers working on digital signal processing tasks are often faced with the question which transform is appropriate for a specific application. To address this question, this paper first briefly introduces the different transforms. Then it compares them with respect to the achievable "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.06707","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/2101.06707/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":"2101.06707","created_at":"2026-07-05T02:07:36.413243+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.06707v1","created_at":"2026-07-05T02:07:36.413243+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.06707","created_at":"2026-07-05T02:07:36.413243+00:00"},{"alias_kind":"pith_short_12","alias_value":"UPFTBOLWHZ3O","created_at":"2026-07-05T02:07:36.413243+00:00"},{"alias_kind":"pith_short_16","alias_value":"UPFTBOLWHZ3OWWIH","created_at":"2026-07-05T02:07:36.413243+00:00"},{"alias_kind":"pith_short_8","alias_value":"UPFTBOLW","created_at":"2026-07-05T02:07:36.413243+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.10407","citing_title":"RadDet: A Wideband Dataset for Real-Time Radar Spectrum Detection","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UPFTBOLWHZ3OWWIH2XPHZXBPWN","json":"https://pith.science/pith/UPFTBOLWHZ3OWWIH2XPHZXBPWN.json","graph_json":"https://pith.science/api/pith-number/UPFTBOLWHZ3OWWIH2XPHZXBPWN/graph.json","events_json":"https://pith.science/api/pith-number/UPFTBOLWHZ3OWWIH2XPHZXBPWN/events.json","paper":"https://pith.science/paper/UPFTBOLW"},"agent_actions":{"view_html":"https://pith.science/pith/UPFTBOLWHZ3OWWIH2XPHZXBPWN","download_json":"https://pith.science/pith/UPFTBOLWHZ3OWWIH2XPHZXBPWN.json","view_paper":"https://pith.science/paper/UPFTBOLW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.06707&json=true","fetch_graph":"https://pith.science/api/pith-number/UPFTBOLWHZ3OWWIH2XPHZXBPWN/graph.json","fetch_events":"https://pith.science/api/pith-number/UPFTBOLWHZ3OWWIH2XPHZXBPWN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UPFTBOLWHZ3OWWIH2XPHZXBPWN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UPFTBOLWHZ3OWWIH2XPHZXBPWN/action/storage_attestation","attest_author":"https://pith.science/pith/UPFTBOLWHZ3OWWIH2XPHZXBPWN/action/author_attestation","sign_citation":"https://pith.science/pith/UPFTBOLWHZ3OWWIH2XPHZXBPWN/action/citation_signature","submit_replication":"https://pith.science/pith/UPFTBOLWHZ3OWWIH2XPHZXBPWN/action/replication_record"}},"created_at":"2026-07-05T02:07:36.413243+00:00","updated_at":"2026-07-05T02:07:36.413243+00:00"}