{"id":"W2106361725","doi":"10.1109/mwscas.2007.4488607","title":"Continuous wavelet transform based source separation","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Short-time Fourier transform; Wavelet transform; Continuous wavelet transform; Harmonic wavelet transform; Constant Q transform; Computer science; Fourier transform; Wavelet; Artificial intelligence; Blind signal separation; Independent component analysis; Pattern recognition (psychology); Time–frequency analysis; Second-generation wavelet transform; Discrete wavelet transform; Source separation; Stationary wavelet transform; S transform; Speech recognition; Mathematics; Fourier analysis; Computer vision; Telecommunications; Channel (broadcasting)","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":[0.0004554123,0.0009182802,0.001047447,0.001006015,0.0003325951,0.001033909,0.0006209236,0.001176557,0.002451105],"category_scores_gemma":[0.001534819,0.0002445595,0.0006971296,0.001249965,0.0004763192,0.001442137,0.0008494022,0.0009707822,0.00233076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001732333,"about_ca_system_score_gemma":0.0004828345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004018427,"about_ca_topic_score_gemma":0.0003744497,"domain_scores_codex":[0.9994729,0.00008253608,0.00002770712,0.0001218875,0.000258511,0.00003637059],"domain_scores_gemma":[0.9996087,0.0001418993,0.00004528402,0.00005827407,0.0001306104,0.00001526925],"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":[0.0002476964,0.0001353342,0.0004354834,0.0005303281,0.000152087,0.0005053923,0.00008369004,0.04041297,0.246628,0.02913083,0.003373257,0.6783648],"study_design_scores_gemma":[0.00004392138,0.0002837621,0.001940138,0.00007611476,0.0001340638,0.002077775,0.00007173549,0.7618377,0.1720529,0.02313116,0.03825675,0.0000939528],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004758489,0.001252165,0.9909791,0.00006673324,0.0001732798,0.00002762967,0.0000407733,0.0002872476,0.002414665],"genre_scores_gemma":[0.1134567,0.00439142,0.8724217,0.000181559,0.000360319,0.00009899889,0.0004391204,0.0001791992,0.008470922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002451105,"threshold_uncertainty_score":0.008199692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.018544916142702,"score_gpt":0.2785451178902901,"score_spread":0.2600002017475881,"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."}}