A Lightweight Deep Learning-Based Approach for Jazz Music Generation in MIDI Format. (5th August 2022)

• Record Type: Journal Article
• Authors: Yadav, Prasant Singh; Khan, Shadab; Singh, Yash Veer; Garg, Puneet; Singh, Ram Sewak
• Other Names: Kumar Vijay Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-08-05
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/2140895
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : In today's real-world, estimation of the level of difficulty of the musical is part of very meaningful musical learning. A musical learner cannot learn without a defined precise estimation. This problem is not very basic but it is complicated up to some extent because of the subjectivity of the contents and the scarcity of the data. In this paper, a lightweight model that generates original music content using deep learning along with generating music based on a specific genre is proposed. The paper discusses a lightweight deep learning-based approach for jazz music generation in MIDI format. In this work, the genre of music chosen is Jazz, and the songs selected are classical numbers composed by various artists. All the songs are in MIDI format and there might be differences in the pace or tone of the music. It is prudential to make sure that the chosen datasets that do not have these kinds of differences and are similar to the final output as desired. A model is trained to take in a part of a music file as input and should produce its continuation. The result generated should be similar to the dataset given as the input. Moreover, the proposed model also generates music using a particular instrument.