{"id":"W2950541290","doi":"10.48550/arxiv.1304.0793","title":"A local fingerprinting approach for audio copy detection","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Scale-invariant feature transform; Identification (biology); Artificial intelligence; Pattern recognition (psychology); Representation (politics); Mel-frequency cepstrum; Speech recognition; Image (mathematics); Computer vision; Feature extraction","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":[0.0002835104,0.0002897739,0.0003113473,0.000192711,0.0003148129,0.0002716726,0.00129595,0.0003093671,0.00001306425],"category_scores_gemma":[0.00003322573,0.0003295602,0.000221557,0.000376154,0.0001138049,0.0004556113,0.001394128,0.000481055,0.00003381484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001992446,"about_ca_system_score_gemma":0.0001674437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008727085,"about_ca_topic_score_gemma":0.000005079924,"domain_scores_codex":[0.998077,0.00005984093,0.000196425,0.001174038,0.00008220425,0.0004105186],"domain_scores_gemma":[0.9985368,0.00007993446,0.0003159564,0.0007416202,0.0001969758,0.0001287243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006011433,0.0002479818,0.0006131369,0.001375944,0.0002341292,0.000054494,0.001065994,0.7800885,0.0007196098,0.09684898,0.001710431,0.1169806],"study_design_scores_gemma":[0.0003106872,0.00002863897,0.00009323699,0.00007448096,0.00003199441,0.00000466396,0.000059739,0.9779578,0.001871131,0.01845138,0.000724525,0.0003916924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03969969,0.00004117996,0.9556698,0.00005308733,0.0004282807,0.0004212445,0.00000232525,0.0003108915,0.00337353],"genre_scores_gemma":[0.9577318,0.00001441558,0.04088246,0.0001649872,0.0001311345,0.000007230075,0.000006862181,0.00001952211,0.001041576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9180321,"threshold_uncertainty_score":0.9999157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06865290649048167,"score_gpt":0.1810831982503262,"score_spread":0.1124302917598445,"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."}}