{"id":"W4302063918","doi":"10.1007/978-3-031-02565-5_4","title":"Convolution and Filtering","year":2013,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on speech and audio processing","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Convolution (computer science); Digital signal processing; Signal processing; Computer science; Representation (politics); Overlap–add method; Multidimensional signal processing; Algorithm; SIGNAL (programming language); Discrete-time signal; Frequency domain; Fourier transform; Domain (mathematical analysis); Discrete Fourier transform (general); Electronic engineering; Artificial intelligence; Computer hardware; Analog signal; Mathematics; Fourier analysis; Computer vision; Short-time Fourier transform; Engineering; Fractional Fourier transform; Signal transfer function","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":[0.0002554225,0.001203185,0.0008819582,0.001206855,0.0005468156,0.002154129,0.0007102959,0.001158275,0.03143486],"category_scores_gemma":[0.0005850334,0.000416875,0.0004497928,0.001198022,0.001222213,0.00194325,0.0008968654,0.00165194,0.02073952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009546239,"about_ca_system_score_gemma":0.0005832212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001139261,"about_ca_topic_score_gemma":0.001827916,"domain_scores_codex":[0.9997005,0.0000286537,0.00001217414,0.0000747548,0.0001603329,0.00002357931],"domain_scores_gemma":[0.9998759,0.00003634499,0.000005933824,0.00002630023,0.00004476004,0.00001063054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000273888,0.00002430395,0.00006838897,0.0002894416,0.00002290564,0.00008336548,0.0001649829,0.002338559,0.006588213,0.3601735,0.1181468,0.5120721],"study_design_scores_gemma":[0.000005607979,0.00002962973,0.0002324502,0.000100715,0.00001331471,0.0003101315,0.00004505547,0.004909404,0.003792389,0.1607285,0.8298122,0.00002070229],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.00187362,0.06183595,0.3857732,0.0016108,0.004415882,0.00004504128,0.0002541983,0.001488672,0.5427025],"genre_scores_gemma":[0.0223945,0.02321647,0.06012698,0.0007041445,0.001852797,0.00006765781,0.0003302099,0.0005808699,0.8907264],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03143486,"threshold_uncertainty_score":0.1051601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01224789062365485,"score_gpt":0.2071627787320623,"score_spread":0.1949148881084075,"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."}}