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

Dong Hwa Kim, NDT Center, Seoul National Science and Technology University, S. Korea.

Manuscript received on 10 November 2022 | Revised Manuscript received on 10 December 2022 | Manuscript Accepted on 15 March 2023 | Manuscript published on 30 March 2023 | PP: 13-25 | Volume-11 Issue-6, March 2023 | Retrieval Number: 100.1/ijrte.E73510111523 | DOI: 10.35940/ijrte.E7351.0311623

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Deep Learning, DCGAN, Music Genre, AI, Education
Scope of the Article: Classification