{"id":"W4388115379","doi":"10.23919/eusipco58844.2023.10289895","title":"A Decomposition-Based Kalman Filter for the Identification of Acoustic Impulse Responses","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Finite impulse response; Kalman filter; Adaptive filter; Computer science; Impulse response; Reverberation; Impulse (physics); Recursive least squares filter; Algorithm; Infinite impulse response; Computational complexity theory; System identification; Digital filter; Speech recognition; Filter (signal processing); Mathematics; Acoustics; Artificial intelligence; Data modeling; 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":[0.0008020362,0.0006464534,0.0007167216,0.0004568636,0.0003535929,0.0004765157,0.000612648,0.000738728,0.001837878],"category_scores_gemma":[0.002005107,0.0003242386,0.0006834089,0.0006004743,0.0003661032,0.0008131395,0.0005413781,0.001077655,0.001155777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004256076,"about_ca_system_score_gemma":0.001499831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006650708,"about_ca_topic_score_gemma":0.006591348,"domain_scores_codex":[0.9995833,0.00009979524,0.00003062199,0.0001241448,0.0001254748,0.00003661259],"domain_scores_gemma":[0.9996486,0.0001450153,0.00003563204,0.00004261795,0.0001141074,0.00001401487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002024967,0.00007305385,0.0009548982,0.0003434268,0.000134741,0.0001284077,0.0001651992,0.3360604,0.04879401,0.04511901,0.004261068,0.5637634],"study_design_scores_gemma":[0.00001374884,0.00007285787,0.0003881046,0.00002209521,0.00002756694,0.00006197867,0.000009549293,0.9844828,0.004780217,0.003983216,0.006130434,0.00002743065],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006926184,0.00008878002,0.9988569,0.0000158822,0.00002033821,0.000009049216,0.00001541383,0.0001221575,0.0001788824],"genre_scores_gemma":[0.1196517,0.0008636442,0.875294,0.00007090204,0.00008909586,0.0001941606,0.0003559508,0.0000811104,0.003399397],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006650708,"threshold_uncertainty_score":0.01322401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02437925544281486,"score_gpt":0.3077640776195825,"score_spread":0.2833848221767676,"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."}}