{"id":"W2674742270","doi":"10.1109/icassp.2017.7953334","title":"Robust multichannel TDOA estimation for speaker localization using the impulsive characteristics of speech spectrum","year":2017,"lang":"en","type":"article","venue":"","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Reverberation; Robustness (evolution); Computer science; Multilateration; Estimator; Speech recognition; Impulse response; Speech enhancement; Frequency domain; Finite impulse response; Impulse (physics); Acoustics; Algorithm; Background noise; Mathematics; 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":[0.00037843,0.0004242634,0.0004474681,0.0004258172,0.0001966514,0.0004220228,0.000432334,0.0004487089,0.0006922619],"category_scores_gemma":[0.00165983,0.0002300998,0.0004650739,0.0004200107,0.0003141281,0.0006547259,0.0005291511,0.0004711894,0.0002954426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002036391,"about_ca_system_score_gemma":0.0005186591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001097626,"about_ca_topic_score_gemma":0.001616768,"domain_scores_codex":[0.9998424,0.00003392316,0.00001060582,0.00004997502,0.00004821135,0.00001489706],"domain_scores_gemma":[0.9996519,0.0001856254,0.00004346579,0.00003518792,0.00006920417,0.00001448473],"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":[0.0003961679,0.00006327232,0.001406983,0.0002655965,0.0001069068,0.0002073225,0.0002349288,0.2306032,0.2402993,0.008270534,0.0008147239,0.5173311],"study_design_scores_gemma":[0.00001201516,0.00007253858,0.000753958,0.00001091029,0.00003001222,0.0001723022,0.00002921818,0.9640765,0.03142299,0.0019665,0.001430388,0.00002262837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01620405,0.0002365051,0.9829962,0.00002791202,0.00002228268,0.000006943147,0.00001376883,0.0001840012,0.0003084393],"genre_scores_gemma":[0.4286053,0.0005230836,0.5690425,0.00003979419,0.0000671391,0.00004351613,0.0001043191,0.00007743428,0.001496828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001097626,"threshold_uncertainty_score":0.002315819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04840213334228604,"score_gpt":0.2923374266425736,"score_spread":0.2439352933002875,"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."}}