{"id":"W2994125460","doi":"10.1109/access.2019.2957572","title":"Investigation of Different CNN-Based Models for Improved Bird Sound Classification","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Animal Vocal Communication and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":143,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Spectrogram; Deep learning; Computer science; Artificial intelligence; Component (thermodynamics); Pattern recognition (psychology); Bioacoustics; Fuse (electrical); Sound (geography); Machine learning; Speech recognition; Acoustics; Engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.00007109762,0.00008007036,0.00009529839,0.00002836626,0.00003127828,0.0000229415,0.0002968337,0.00009571433,0.000008411365],"category_scores_gemma":[0.00001176974,0.00007023005,0.00006327037,0.00004638019,0.00004348633,0.000009944897,0.0000338625,0.00003898256,0.000003329696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000103992,"about_ca_system_score_gemma":0.00003928382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001610577,"about_ca_topic_score_gemma":0.00003055461,"domain_scores_codex":[0.9994861,0.0000289757,0.0001713782,0.000169834,0.00005589495,0.00008782286],"domain_scores_gemma":[0.9993243,0.00001813808,0.0001272461,0.000359404,0.0001347697,0.0000361286],"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":[0.00008909668,0.00003309657,0.01281825,0.00002868002,0.000007025683,5.581981e-9,0.000008392602,0.00008112819,0.9859095,0.0002799261,0.000118408,0.0006264892],"study_design_scores_gemma":[0.0007461336,0.0002759985,0.03038743,0.00001191647,0.00002364983,1.486934e-7,0.00001927992,0.03282161,0.9336556,0.00166518,0.0002485303,0.0001445277],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9576679,0.00003212408,0.04153125,0.0001640571,0.00008231415,0.0003886286,0.00001430264,0.00001480301,0.0001046435],"genre_scores_gemma":[0.9989259,0.00001088654,0.0004296478,0.0002549507,0.00003495866,0.00007200632,0.0001391976,0.00001741011,0.0001150844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0522539,"threshold_uncertainty_score":0.2863898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1113118563195183,"score_gpt":0.3397779097872294,"score_spread":0.228466053467711,"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."}}