A Study on an Effective Teaching of AI using Google Colab-Based DCGAN Deep Learning Model Building for Music Data Analysis and Genre Classification

26224ab4184a136f282-139e 20230211042619322 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 03 30 2023 11 6 A Study on an Effective Teaching of AI using Google Colab-Based DCGAN Deep Learning Model Building for Music Data Analysis and Genre Classification NDT Center, Seoul National Science and Technology University, S. Korea Dong Hwa Kim https://orcid.org/0000-0002-0528-6736 This paper deals with an effective teaching method of deep learning using theory and Python in the University. Currently, AI and related technology penetrate into all areas such as manufacturing, fashion, design, medical, novel, agriculture, as well as picture and engineering areas. These AI technologies are strongly connected with the education of universities and K-12. There are two categories of AI-related education. The first one is AI-supported education; another thing is education (teaching and learning) to understand AI. In any case, AI and its application method should be taught with theory and performed with S/W. This paper provides a method on how teachers of universities can teach deep learning well with S/W (Python) matching theory. To present the characteristics of deep learning, this paper uses DCGAN and suggests a teaching method with Google Colab easily. This paper analyzes the dataset with visuals and classifies genres to show characteristics between music and the function of deep learning for students' understanding using DCGAN and the music dataset. The results classify music genres by deep learning well. 03 30 2023 13 25 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true Yes, received funds/grants/financial support for this article. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2021R1F1A1056145). The author thanks to supporting of the Korean government (MSIT). No conflicts of interest to the best of our knowledge. The author must submit a statement that the study does not require ethical approval and consent to participate with evidence. Not relevant. I am only the sole author of the article. 10.35940/ijrte.E7351.0311623 https://www.ijrte.org/portfolio-item/E73510111523/