{"id":"W2119076085","doi":"10.1109/icassp.2008.4518650","title":"A pitch extraction algorithm in noise based on temporal and spectral representations","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Pitch detection algorithm; Computer science; Noise (video); Speech recognition; Harmonic; Algorithm; Spectral density estimation; Representation (politics); Frequency domain; Discrete cosine transform; Fundamental frequency; Noise measurement; Speech processing; Pattern recognition (psychology); Mathematics; Artificial intelligence; Noise reduction; Acoustics; Fourier transform; Computer vision; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002852003,0.0002159075,0.000215828,0.0003053609,0.0002817322,0.0003200264,0.0005668569,0.00008727583,0.000012721],"category_scores_gemma":[0.0001049483,0.0001772354,0.00004987156,0.0003104376,0.0002204952,0.0008216681,0.00008441909,0.0003770168,0.000001739917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007234322,"about_ca_system_score_gemma":0.0001829949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003947385,"about_ca_topic_score_gemma":0.000003079521,"domain_scores_codex":[0.9982442,0.00001202501,0.0003853008,0.0005006594,0.0006083807,0.0002493946],"domain_scores_gemma":[0.9989132,0.00007375812,0.0003460616,0.0000947158,0.0004816085,0.00009068067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004163937,0.001073728,0.04919295,0.0003996067,0.00005706383,0.00008962296,0.003376425,0.001894285,0.5331172,0.004826706,0.0007751861,0.4047808],"study_design_scores_gemma":[0.0009824388,0.0002017312,0.01511808,0.0007282005,0.00001535752,0.0001628171,0.000475787,0.8351591,0.1357115,0.01108058,0.00002161106,0.0003427501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7615003,0.00008393588,0.2217172,0.004067799,0.000499823,0.0004027236,0.00001498973,0.0001304723,0.01158277],"genre_scores_gemma":[0.9446181,0.00006041385,0.05466355,0.0002910886,0.0001484687,0.00001041832,0.000001478488,0.00001220753,0.0001942692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8332649,"threshold_uncertainty_score":0.7227448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03922867179786915,"score_gpt":0.2943532713493157,"score_spread":0.2551245995514466,"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."}}