{"id":"W2654966644","doi":"10.1109/ccece.2017.7946622","title":"A null space approach for complete and over-complete blind source separation of autoregressive source signals","year":2017,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Blind signal separation; Autoregressive model; Non-negative matrix factorization; Source separation; Matrix decomposition; Matrix (chemical analysis); Algorithm; Null (SQL); Mathematics; Representation (politics); Gaussian; Applied mathematics; Computer science; Eigenvalues and eigenvectors; Speech recognition; Statistics; Physics; 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":[0.0006601391,0.0002361856,0.000389948,0.0001583446,0.0005161643,0.0006316239,0.001106756,0.0001349204,0.00001118883],"category_scores_gemma":[0.0001260929,0.0002120317,0.0001172271,0.00008347209,0.0002245695,0.0008530855,0.0004205649,0.0001353118,0.000003649411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002331283,"about_ca_system_score_gemma":0.0000680187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001343032,"about_ca_topic_score_gemma":0.00001765451,"domain_scores_codex":[0.9983004,0.000105023,0.0003813465,0.0005773855,0.0003444256,0.0002914472],"domain_scores_gemma":[0.997652,0.0002220111,0.0006855232,0.001107865,0.0002140274,0.000118609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000604995,0.0009525491,0.007370308,0.0006190604,0.000443755,0.000005990265,0.0272675,0.007638533,0.119565,0.7423803,0.03483806,0.05831394],"study_design_scores_gemma":[0.001701134,0.0003472458,0.002909194,0.00004220329,0.00002380753,0.00001661311,0.00009866188,0.9417452,0.0217046,0.005376974,0.02559066,0.0004437057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02429343,0.00005294787,0.9677753,0.0008582306,0.00004153373,0.0008648577,0.00001577494,0.0002549247,0.00584303],"genre_scores_gemma":[0.7073072,0.000005734738,0.2901055,0.000312469,0.00005901723,0.00007868693,0.00001497913,0.00001920259,0.002097322],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9341066,"threshold_uncertainty_score":0.8646402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05721603076779215,"score_gpt":0.3289003429340221,"score_spread":0.27168431216623,"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."}}