{"id":"W2280812555","doi":"10.1007/978-3-319-12976-1_27","title":"Waveform-Aligned Adaptive Windows for Spectral Component Tracking and Noise Rejection","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Short-time Fourier transform; Computer science; Spectrogram; Upsampling; Pipeline (software); Waveform; Algorithm; SIGNAL (programming language); Noise (video); Speech recognition; Fourier transform; Fourier analysis; Artificial intelligence; Mathematics; Telecommunications","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008979431,0.0005187119,0.0005651285,0.0006260403,0.0004857162,0.0007762325,0.001429588,0.0002723902,0.000003584105],"category_scores_gemma":[0.0000745554,0.0004646683,0.0001357309,0.0003317762,0.0004743668,0.0006868458,0.0005283902,0.0004037931,0.000007204829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002851566,"about_ca_system_score_gemma":0.0002826538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001551715,"about_ca_topic_score_gemma":0.00005838856,"domain_scores_codex":[0.9966074,0.00002516312,0.0004793481,0.001514265,0.0006504806,0.0007233609],"domain_scores_gemma":[0.9981237,0.0003912023,0.0003602119,0.0006855337,0.0002446326,0.0001947286],"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":[0.00002926801,0.00001799806,0.00003274728,0.00006763048,0.00001339672,0.00002053655,0.0006800124,0.004869257,0.002863705,0.007803977,0.00001503853,0.9835864],"study_design_scores_gemma":[0.001051173,0.0008142126,0.0004243091,0.0009208126,0.00002437805,0.00025909,7.969642e-7,0.6961946,0.07992586,0.2177864,0.001340042,0.001258285],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005337039,0.0003394907,0.9952661,0.0006940552,0.001275377,0.0005988273,0.000004004673,0.0001673327,0.001121075],"genre_scores_gemma":[0.3629616,0.00002554062,0.634806,0.00107265,0.0008819213,0.0000161937,0.00000508225,0.00003936359,0.0001916528],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9823282,"threshold_uncertainty_score":0.9997805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01899624211202839,"score_gpt":0.2391794215796292,"score_spread":0.2201831794676008,"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."}}