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Audio detection using machine learning & transfer learning models
(Kadir Has Üniversitesi, 2021) Dağ, Hasan; Dağ, Hasan
In this paper, using datasets ESC-50 & ESC-10 of environmental sounds, machine learning algorithms, and feature extraction methods are used to develop recognition performance. K-NN, SVM, Random Forest are used for comparing the recognition results. The different feature extraction methods in the literature are used to get more meaningful attributes from these datasets and obtain a higher accuracy rate. This approach shows that SVM algorithm has a significantly good result with accuracy scores. The best accuracy scores obtained by classic machine learning algorithms are %42,15 for ESC-50 and %77,7 for ESC-10. In addition to this, the experiments have been done with a pre-trained ResNet neural network as a backbone, which achieves successful results despite the machine learning models. In this study, a higher accuracy rate is achieved from baseline machine learning algorithms in literature and using transfer learning with pre-trained Resnet backbones to reach some state of art results. The accuracy scores are %68,95 for ESC-50 and %87,25 for ESC-10.