{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001102846,0.001050547,0.0007218915,0.001338593,0.0005412715,0.0009930488,0.0008635477,0.0008603488,0.002602801],"category_scores_gemma":[0.002350301,0.0003291267,0.001014376,0.000802628,0.001331065,0.001748811,0.001326681,0.001307421,0.001127757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003896152,"about_ca_system_score_gemma":0.0007773304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008575635,"about_ca_topic_score_gemma":0.0008713523,"domain_scores_codex":[0.9991348,0.0002533512,0.00005646672,0.0001555394,0.0003594283,0.00004042733],"domain_scores_gemma":[0.9990374,0.0003422063,0.00009550578,0.0001929805,0.0002970751,0.00003493938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002426218,0.0001102106,0.0005587873,0.0003375931,0.0001891846,0.0002349803,0.0003036411,0.1426405,0.05429365,0.3202768,0.004684085,0.476128],"study_design_scores_gemma":[0.00002382354,0.0001480897,0.0004920334,0.00003015268,0.0000445211,0.0005815436,0.00006708442,0.8436755,0.02208742,0.1133792,0.01935718,0.0001133467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009047316,0.00011657,0.9983296,0.00002783939,0.00002239229,0.000005418076,0.00001458256,0.00007941277,0.0004994806],"genre_scores_gemma":[0.06474692,0.0006546904,0.9298043,0.000120406,0.0001620809,0.00009538384,0.0002514492,0.0001245874,0.004040191],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002602801,"threshold_uncertainty_score":0.008707285,"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."}}