{"id":"W2249004118","doi":"10.1007/s11042-015-3081-8","title":"A spectrogram-based audio fingerprinting system for content-based copy detection","year":2015,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Music and Audio Processing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Spectrogram; Robustness (evolution); Pattern recognition (psychology); Artificial intelligence; Fingerprint (computing); Noise (video); Audio signal; Binary number; Speech recognition; Image (mathematics); Speech coding; Mathematics","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":[0.0005625019,0.0006300291,0.0009727145,0.001843871,0.0004593775,0.0008046796,0.001228153,0.00115012,0.006655709],"category_scores_gemma":[0.001191163,0.0003180735,0.0002671925,0.0008702778,0.0002071494,0.0009480755,0.0005838734,0.0004850074,0.005437457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003269977,"about_ca_system_score_gemma":0.0005448989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001210558,"about_ca_topic_score_gemma":0.001889022,"domain_scores_codex":[0.9995077,0.00004788637,0.00003014979,0.0001425096,0.0002343112,0.00003740083],"domain_scores_gemma":[0.9990152,0.0001934575,0.00008846234,0.0001943759,0.0004212829,0.00008725222],"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":[0.0006884126,0.0002027327,0.002223962,0.0001768789,0.00006064657,0.0001467788,0.00005195263,0.0007000398,0.635335,0.0005005156,0.005050482,0.3548625],"study_design_scores_gemma":[0.0002359371,0.0008999207,0.01443676,0.00008256156,0.0003375227,0.002855887,0.00008424166,0.1749049,0.7770935,0.0009161616,0.02794263,0.0002098196],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07813462,0.001289285,0.8910996,0.0002130826,0.0003872498,0.0003739707,0.001292089,0.02262027,0.004589943],"genre_scores_gemma":[0.4197195,0.0008099263,0.5655462,0.0005257189,0.0002991291,0.0003546011,0.001439736,0.0005910701,0.01071414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006655709,"threshold_uncertainty_score":0.02226561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08087947141446279,"score_gpt":0.2723182795589802,"score_spread":0.1914388081445175,"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."}}