{"id":"W2125655214","doi":"10.1109/icassp.2008.4517572","title":"A Computationally Efficient Scheme for Dominant Harmonic Source Separation","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"","keywords":"Source separation; Computer science; Scheme (mathematics); Harmonic; Blind signal separation; Harmonic analysis; Separation (statistics); Feature (linguistics); Speech recognition; Music information retrieval; Artificial intelligence; Algorithm; Electronic engineering; Musical; Acoustics; Machine learning; Mathematics; Engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003673791,0.0002459144,0.0002575988,0.0001773933,0.0005495241,0.000364348,0.0009779658,0.00009151719,0.000007674756],"category_scores_gemma":[0.0001318241,0.0001934417,0.0001022685,0.0002356621,0.0002382415,0.0004836662,0.0001530494,0.0002272343,0.000003641523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007962741,"about_ca_system_score_gemma":0.0002946042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000420786,"about_ca_topic_score_gemma":3.787769e-7,"domain_scores_codex":[0.998009,0.000007718059,0.0004696548,0.0005033488,0.0007099669,0.0003003374],"domain_scores_gemma":[0.9975671,0.00009344014,0.000553151,0.0000891864,0.001603456,0.00009365751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002507035,0.0003370093,0.0007621809,0.0003634392,0.00007460319,0.000004023106,0.002364036,0.003840628,0.9146286,0.01297893,0.001104329,0.06329148],"study_design_scores_gemma":[0.000742787,0.0001279523,0.0005262399,0.0004033045,0.00001895231,0.0001048528,0.0002201163,0.7592028,0.2315529,0.006723847,0.0001114067,0.0002648488],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4032621,0.0000751011,0.5930524,0.001453603,0.0002730277,0.0003097194,0.000008818494,0.00006913992,0.001496053],"genre_scores_gemma":[0.911572,0.00002690502,0.08736829,0.0003219116,0.0002045339,0.00002250966,0.000002548449,0.00001608624,0.00046522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7553622,"threshold_uncertainty_score":0.7888324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04563240306891411,"score_gpt":0.2956046172494156,"score_spread":0.2499722141805014,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}