{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:Z75Q24SSS7MGOTV6VDP7QN35DR","short_pith_number":"pith:Z75Q24SS","schema_version":"1.0","canonical_sha256":"cffb0d725297d8674ebea8dff8377d1c612b94cf30a5a7066fbc6a7275c40543","source":{"kind":"arxiv","id":"1609.09799","version":2},"attestation_state":"computed","paper":{"title":"Optimal spectral transportation with application to music transcription","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SD"],"primary_cat":"stat.ML","authors_text":"C\\'edric F\\'evotte, Nicolas Courty, R\\'emi Flamary, Valentin Emiya","submitted_at":"2016-09-30T16:28:12Z","abstract_excerpt":"Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates. In particular, state-of-the-art music transcription systems decompose the spectrogram of the input signal onto a dictionary of representative note spectra. The typical measures of fit used to quantify the adequacy of the decomposition compare the data and template entries frequency-wise. As such, small displacements of energy from a frequency bin to another as well as variations of timber can disproportionally harm the fit. We address these issues by means of optimal "},"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":"1609.09799","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-09-30T16:28:12Z","cross_cats_sorted":["cs.LG","cs.SD"],"title_canon_sha256":"ac537d4d91327f7d8a5469e727f81e89582c69450fb5f0046e4b8a1e2696a520","abstract_canon_sha256":"afe5de2ac7e238e089fe80e6e156ee1da23765f3773d23b84bc6e6595f618c4e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:02:51.668613Z","signature_b64":"G6h0fvPoTeXHvG7bLxMzigyaGaSzAi/hkP0y8Tu7l9wPVWJFD29/nwRZNV9YyRocPnruTFgJ7U2S5ClzkTFeCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cffb0d725297d8674ebea8dff8377d1c612b94cf30a5a7066fbc6a7275c40543","last_reissued_at":"2026-05-18T01:02:51.668003Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:02:51.668003Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimal spectral transportation with application to music transcription","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SD"],"primary_cat":"stat.ML","authors_text":"C\\'edric F\\'evotte, Nicolas Courty, R\\'emi Flamary, Valentin Emiya","submitted_at":"2016-09-30T16:28:12Z","abstract_excerpt":"Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates. In particular, state-of-the-art music transcription systems decompose the spectrogram of the input signal onto a dictionary of representative note spectra. The typical measures of fit used to quantify the adequacy of the decomposition compare the data and template entries frequency-wise. As such, small displacements of energy from a frequency bin to another as well as variations of timber can disproportionally harm the fit. We address these issues by means of optimal "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.09799","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1609.09799","created_at":"2026-05-18T01:02:51.668088+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.09799v2","created_at":"2026-05-18T01:02:51.668088+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.09799","created_at":"2026-05-18T01:02:51.668088+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z75Q24SSS7MG","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z75Q24SSS7MGOTV6","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z75Q24SS","created_at":"2026-05-18T12:30:53.716459+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/Z75Q24SSS7MGOTV6VDP7QN35DR","json":"https://pith.science/pith/Z75Q24SSS7MGOTV6VDP7QN35DR.json","graph_json":"https://pith.science/api/pith-number/Z75Q24SSS7MGOTV6VDP7QN35DR/graph.json","events_json":"https://pith.science/api/pith-number/Z75Q24SSS7MGOTV6VDP7QN35DR/events.json","paper":"https://pith.science/paper/Z75Q24SS"},"agent_actions":{"view_html":"https://pith.science/pith/Z75Q24SSS7MGOTV6VDP7QN35DR","download_json":"https://pith.science/pith/Z75Q24SSS7MGOTV6VDP7QN35DR.json","view_paper":"https://pith.science/paper/Z75Q24SS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.09799&json=true","fetch_graph":"https://pith.science/api/pith-number/Z75Q24SSS7MGOTV6VDP7QN35DR/graph.json","fetch_events":"https://pith.science/api/pith-number/Z75Q24SSS7MGOTV6VDP7QN35DR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z75Q24SSS7MGOTV6VDP7QN35DR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z75Q24SSS7MGOTV6VDP7QN35DR/action/storage_attestation","attest_author":"https://pith.science/pith/Z75Q24SSS7MGOTV6VDP7QN35DR/action/author_attestation","sign_citation":"https://pith.science/pith/Z75Q24SSS7MGOTV6VDP7QN35DR/action/citation_signature","submit_replication":"https://pith.science/pith/Z75Q24SSS7MGOTV6VDP7QN35DR/action/replication_record"}},"created_at":"2026-05-18T01:02:51.668088+00:00","updated_at":"2026-05-18T01:02:51.668088+00:00"}