Paper Title
AI BASED MUSIC TUNESMITH
Article Identifiers
Authors
Shobhit sharma , Amritander Pratap Singh , Ramesh vaish , Aman singh , Anamika jadav
Keywords
Artificial intelligence
Abstract
Generating music has some distinct differences from generating images and videos. To begin with, it is an art of time, so a temporal model is essential. Additionally, music is often composed of multiple instruments/tracks with their own tempo, which all unfold over time in a mutually dependent manner. Musical notes are also often grouped into chords, arpeggios, or melodies in polyphonic music, thus introducing a hierarchical ordering of notes. In this paper, we propose three models for symbolic multi-track music generation based on a framework of generative adversarial networks (GANS). The three models differ in their underlying assumptions and corresponding network architectures, and are referred to as the jamming model, the composer model, and the hybrid model. We trained the proposed models on a dataset of more than one hundred thousand bars of rock music, and applied them to generate piano rolls.
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How To Cite (APA)
Shobhit sharma , Amritander Pratap Singh , Ramesh vaish, Aman singh, & Anamika jadav (February-2023). AI BASED MUSIC TUNESMITH . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(2), c53-c56. https://ijnrd.org/papers/IJNRD2302209.pdf
Issue
Volume 8 Issue 2, February-2023
Pages : c53-c56
Other Publication Details
Paper Reg. ID: IJNRD_187694
Published Paper Id: IJNRD2302209
Downloads: 000121976
Research Area: Science & Technology
Country: Lucknow , Uttar Pradesh , India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2302209.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2302209
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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