{"id":"W1581433892","doi":"10.1109/acssc.2003.1291969","title":"Transient detection of audio signals based on an adaptive comb filter in the frequency domain","year":2004,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Energy (signal processing); Transient (computer programming); Audio signal; SIGNAL (programming language); Comb filter; Envelope (radar); Time domain; Frequency domain; Residual; Envelope detector; Audio signal processing; Speech recognition; Spectrogram; Adaptive filter; Filter (signal processing); Time–frequency analysis; Acoustics; Algorithm; Computer vision; Telecommunications; Physics; Speech coding; Amplifier","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003974828,0.00008087775,0.00009750613,0.0000799331,0.00006290456,0.00004002205,0.0003674187,0.00003332621,0.00002029431],"category_scores_gemma":[0.000005640048,0.00005132829,0.00003768888,0.0003232172,0.0000371407,0.0002509613,0.000009367663,0.00009512773,0.000004443958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003309115,"about_ca_system_score_gemma":0.000062503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001375308,"about_ca_topic_score_gemma":0.0001659304,"domain_scores_codex":[0.9991613,0.000100595,0.0001629586,0.0002011073,0.0002367879,0.0001372367],"domain_scores_gemma":[0.999579,0.000053676,0.00005442696,0.0002550546,0.00003082545,0.00002706813],"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":[0.0002194462,0.002654806,0.0003001136,0.0001074387,0.0000314012,0.0001244751,0.04915794,0.08276022,0.4594443,0.1958358,0.0001951348,0.2091689],"study_design_scores_gemma":[0.005011283,0.00515178,0.02343064,0.000489512,0.00001719813,0.00002457473,0.001739308,0.1361369,0.5856863,0.2412008,0.0002537592,0.0008578249],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1513081,0.00001099847,0.8418363,0.001134826,0.00004343935,0.0001122007,6.196618e-7,0.00002594054,0.00552765],"genre_scores_gemma":[0.9644412,3.741643e-7,0.03304842,0.002475809,0.00001625368,0.000009712125,3.935326e-7,0.000002888206,0.000004926598],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8131332,"threshold_uncertainty_score":0.2093107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02291237340646997,"score_gpt":0.2383383767825295,"score_spread":0.2154260033760595,"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."}}