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 discusses an effective teaching method for deep learning using theory and Python at the University Level. Currently, AI and related technology penetrate all areas such as manufacturing, fashion, design, medicine, novel, agriculture, as well as picture and engineering. These AI technologies are closely tied to the education of universities and K-12 schools. There are two categories of AI-related education. The first one is AI-supported education; the other is education (teaching and learning) to understand AI. In any case, AI and its application method should be taught through theory and implemented with software. This paper presents a method for university teachers to effectively teach deep learning using software (Python) and matching theory. To show the characteristics of deep learning, this paper utilises DCGAN and proposes a teaching method that can be easily implemented with Google Colab. This paper analyses the dataset using visuals and classifies genres to illustrate the characteristics of music and the application of deep learning for students’ understanding, utilising DCGAN and a music dataset. The results classify music genres effectively using deep learning.
Keywords: Deep Learning, DCGAN, Music Genre, AI, Education
Scope of the Article: Classification