{"id":"W2150796234","doi":"10.1109/icassp.2008.4517553","title":"Blind source separation in a distributed microphone meeting environment for improved teleconferencing","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Teleconference; Computer science; Microphone; Blind signal separation; Exploit; Scheme (mathematics); Speech recognition; Source separation; Perspective (graphical); Noise (video); Artificial intelligence; Multimedia; 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":[0.0005467534,0.000447469,0.0004419648,0.00025254,0.0003157772,0.0004812943,0.0006601926,0.0007515731,0.001902448],"category_scores_gemma":[0.001341669,0.0002667328,0.0003060855,0.0003125264,0.0003209953,0.0008864365,0.001071294,0.0006750475,0.001137094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002069287,"about_ca_system_score_gemma":0.0004697058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003826213,"about_ca_topic_score_gemma":0.0007851106,"domain_scores_codex":[0.9995352,0.0001984823,0.00001554258,0.00007244312,0.0001396863,0.00003880838],"domain_scores_gemma":[0.9995982,0.0001756237,0.00003194989,0.00006148028,0.00009629886,0.00003649221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001130208,0.0002333454,0.0005094209,0.0001220552,0.00003707063,0.0003977633,0.0003105331,0.07728042,0.6672238,0.008561889,0.001591776,0.2426019],"study_design_scores_gemma":[0.0001807224,0.0007240825,0.001377844,0.00002277776,0.00003979705,0.0006818466,0.00009625334,0.7843125,0.2016922,0.003586156,0.00721298,0.00007279733],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07717913,0.0001260147,0.9191517,0.0001286819,0.00005985336,0.00003176757,0.00002406015,0.0008308155,0.002467832],"genre_scores_gemma":[0.4012588,0.0001544343,0.5943551,0.00009838164,0.00007414651,0.00005924355,0.00007585173,0.00008315982,0.003840844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001902448,"threshold_uncertainty_score":0.006364346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03813966876019507,"score_gpt":0.280738313503972,"score_spread":0.2425986447437769,"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."}}