{"id":"W1850832570","doi":"10.1109/imtc.2005.1604322","title":"Acoustic Reflections Detection for Microphone Array Applications","year":2005,"lang":"en","type":"article","venue":"2005 IEEE Instrumentationand Measurement Technology Conference Proceedings","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Beamforming; Microphone; Lag; Computer science; Microphone array; Acoustics; Speech recognition; Power (physics); Telecommunications; Physics","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":[0.000467181,0.001139281,0.0005018952,0.0005656619,0.0002503545,0.0009032871,0.0009120778,0.001629041,0.007051261],"category_scores_gemma":[0.002094429,0.0004344489,0.0003857058,0.0006831702,0.0002693859,0.0009100127,0.0004544045,0.0007752004,0.005269291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002633804,"about_ca_system_score_gemma":0.0003772576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002674372,"about_ca_topic_score_gemma":0.0005593248,"domain_scores_codex":[0.9992924,0.0001928861,0.00002461458,0.0001330365,0.0003095475,0.00004745245],"domain_scores_gemma":[0.9989135,0.0004717392,0.0001047975,0.0001014741,0.0003632381,0.00004524951],"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":[0.0005130476,0.00008259474,0.001086604,0.0006077092,0.00006600532,0.0002630002,0.00008244679,0.007201909,0.5270902,0.003040627,0.005932476,0.4540334],"study_design_scores_gemma":[0.0001444177,0.00110564,0.004922079,0.0001646785,0.0002108171,0.002918663,0.0001546924,0.2343737,0.6786817,0.00842044,0.06872986,0.0001732187],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01491463,0.003621962,0.9715012,0.0003011409,0.0003544066,0.0000893774,0.00023783,0.002620057,0.006359319],"genre_scores_gemma":[0.239188,0.00383651,0.7455123,0.0006640496,0.0005614355,0.0001640297,0.0004974949,0.0001847312,0.009391419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007051261,"threshold_uncertainty_score":0.02358884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03973955948319216,"score_gpt":0.2802349315631585,"score_spread":0.2404953720799664,"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."}}