{"id":"W3095791400","doi":"10.1007/978-3-030-63128-4_30","title":"Identification of Drone Payload Using Mel-Frequency Cepstral Coefficients and LSTM Neural Networks","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Drone; Payload (computing); Computer science; Mel-frequency cepstrum; Identification (biology); Software deployment; Artificial neural network; Speech recognition; Artificial intelligence; Feature (linguistics); SIGNAL (programming language); Real-time computing; Feature extraction; Computer security","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":[0.0001294833,0.0005557294,0.0003084186,0.0004625547,0.0001783075,0.0005422509,0.0003083842,0.0004339147,0.001896572],"category_scores_gemma":[0.0004504109,0.0001998489,0.0002980741,0.0003674757,0.0001223153,0.0006053013,0.0002845782,0.0004684269,0.001777099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001782293,"about_ca_system_score_gemma":0.0001722906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002227332,"about_ca_topic_score_gemma":0.003671265,"domain_scores_codex":[0.9999403,0.000005455232,0.000003159831,0.000017651,0.00002413931,0.000009209432],"domain_scores_gemma":[0.9999174,0.00002129779,0.00001102816,0.00001184388,0.00003479205,0.000003608888],"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":[0.0001347148,0.00005836878,0.001690097,0.0001998701,0.00004555539,0.0002344973,0.00008156239,0.08325396,0.1163386,0.001996198,0.003939467,0.7920271],"study_design_scores_gemma":[0.0000055391,0.0000561372,0.004794814,0.00005389027,0.00003011055,0.0002604106,0.00006137037,0.9542433,0.03337352,0.00158532,0.005514836,0.00002072326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05792158,0.001576174,0.9277179,0.000119476,0.0002498975,0.00006315381,0.0003832115,0.001255157,0.01071342],"genre_scores_gemma":[0.7263618,0.002110081,0.2475747,0.0001266202,0.0001253155,0.00007290835,0.001253856,0.0001846728,0.02219013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002227332,"threshold_uncertainty_score":0.006344616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01292495848093229,"score_gpt":0.2367242155972469,"score_spread":0.2237992571163146,"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."}}