{"id":"W1966610007","doi":"10.1109/pacrim.2013.6625506","title":"A nested microphone array for broadband audio signal processing","year":2013,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Broadband; Computer science; Microphone array; Microphone; SIGNAL (programming language); Acoustics; Signal processing; Beamforming; Frequency domain; Audio signal processing; Set (abstract data type); Audio signal; Speech recognition; Telecommunications; Physics; Speech coding; Computer vision","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":[0.0007184469,0.0007363709,0.0006286219,0.0002694193,0.0001360982,0.0003595993,0.0007790965,0.000804943,0.00303446],"category_scores_gemma":[0.001625082,0.0003567242,0.0003636747,0.0002515348,0.0002726605,0.0006927922,0.0005925109,0.0004283997,0.001884026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000186027,"about_ca_system_score_gemma":0.0003968407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002959754,"about_ca_topic_score_gemma":0.0007297769,"domain_scores_codex":[0.9990585,0.0002358925,0.00004102646,0.0001756168,0.0004550057,0.0000340825],"domain_scores_gemma":[0.9993646,0.0001968433,0.00005585348,0.00009895449,0.0002358453,0.00004790395],"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":[0.000169865,0.00003786393,0.0002341302,0.0001630186,0.00002317875,0.0001251042,0.00005373169,0.003236844,0.9148617,0.001463137,0.0004241716,0.07920713],"study_design_scores_gemma":[0.0001480434,0.002106353,0.004491253,0.0001003045,0.0001740358,0.005234387,0.00005568093,0.254864,0.683311,0.002902504,0.04644948,0.0001629063],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01060115,0.0003454876,0.9870268,0.00005022437,0.00009264115,0.00008340467,0.00007092099,0.0006442092,0.001085194],"genre_scores_gemma":[0.06759769,0.0002722447,0.9299919,0.00008188213,0.00003530928,0.0001150137,0.0001087293,0.00003603513,0.001761159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00303446,"threshold_uncertainty_score":0.01015133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01292164677139916,"score_gpt":0.2306198128040589,"score_spread":0.2176981660326598,"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."}}