ComposeOn Academy: Transforming Melodic Ideas into Complete Compositions Integrating Music Learning

• URL: http://arxiv.org/abs/2502.15255v1
• Date: Fri, 21 Feb 2025 07:18:19 GMT
• Title: ComposeOn Academy: Transforming Melodic Ideas into Complete Compositions Integrating Music Learning
• Authors: Hongxi Pu, Futian Jiang, Zihao Chen, Xingyue Song,
• Abstract summary: ComposeOn is a music theory-based tool designed for users with limited musical knowledge.<n>By integrating music theory, it explains music creation at beginner, intermediate, and advanced levels.
• Score: 2.1311710788645613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Music composition has long been recognized as a significant art form. However, existing digital audio workstations and music production software often present high entry barriers for users lacking formal musical training. To address this, we introduce ComposeOn, a music theory-based tool designed for users with limited musical knowledge. ComposeOn enables users to easily extend their melodic ideas into complete compositions and offers simple editing features. By integrating music theory, it explains music creation at beginner, intermediate, and advanced levels. Our user study (N=10) compared ComposeOn with the baseline method, Suno AI, demonstrating that ComposeOn provides a more accessible and enjoyable composing and learning experience for individuals with limited musical skills. ComposeOn bridges the gap between theory and practice, offering an innovative solution as both a composition aid and music education platform. The study also explores the differences between theory-based music creation and generative music, highlighting the former's advantages in personal expression and learning.

Related papers

• Maestoso: An Intelligent Educational Sketching Tool for Learning Music Theory [5.744168250632383]
  Maestoso is an educational tool for novice learners to learn music theory through sketching practice. We show that Maestoso performs reasonably well on recognizing music structure elements and that novice students can comfortably grasp introductory music theory in a single session.
  arXiv  Detail & Related papers  (2025-04-04T20:46:24Z)
• A Survey of Foundation Models for Music Understanding [60.83532699497597]
  This work is one of the early reviews of the intersection of AI techniques and music understanding. We investigated, analyzed, and tested recent large-scale music foundation models in respect of their music comprehension abilities.
  arXiv  Detail & Related papers  (2024-09-15T03:34:14Z)
• Towards Explainable and Interpretable Musical Difficulty Estimation: A Parameter-efficient Approach [49.2787113554916]
  Estimating music piece difficulty is important for organizing educational music collections. Our work employs explainable descriptors for difficulty estimation in symbolic music representations. Our approach, evaluated in piano repertoire categorized in 9 classes, achieved 41.4% accuracy independently, with a mean squared error (MSE) of 1.7.
  arXiv  Detail & Related papers  (2024-08-01T11:23:42Z)
• ComposerX: Multi-Agent Symbolic Music Composition with LLMs [51.68908082829048]
  Music composition is a complex task that requires abilities to understand and generate information with long dependency and harmony constraints. Current LLMs easily fail in this task, generating ill-written music even when equipped with modern techniques like In-Context-Learning and Chain-of-Thoughts. We propose ComposerX, an agent-based symbolic music generation framework.
  arXiv  Detail & Related papers  (2024-04-28T06:17:42Z)
• MusicRL: Aligning Music Generation to Human Preferences [62.44903326718772]
  MusicRL is the first music generation system finetuned from human feedback. We deploy MusicLM to users and collect a substantial dataset comprising 300,000 pairwise preferences. We train MusicRL-U, the first text-to-music model that incorporates human feedback at scale.
  arXiv  Detail & Related papers  (2024-02-06T18:36:52Z)
• MeloForm: Generating Melody with Musical Form based on Expert Systems and Neural Networks [146.59245563763065]
  MeloForm is a system that generates melody with musical form using expert systems and neural networks. It can support various kinds of forms, such as verse and chorus form, rondo form, variational form, sonata form, etc.
  arXiv  Detail & Related papers  (2022-08-30T15:44:15Z)
• MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training [97.91071692716406]
  Symbolic music understanding refers to the understanding of music from the symbolic data. MusicBERT is a large-scale pre-trained model for music understanding.
  arXiv  Detail & Related papers  (2021-06-10T10:13:05Z)
• Music Embedding: A Tool for Incorporating Music Theory into Computational Music Applications [0.3553493344868413]
  It is important to digitally represent music in a music theoretic and concise manner. Existing approaches for representing music are ineffective in terms of utilizing music theory.
  arXiv  Detail & Related papers  (2021-04-24T04:32:45Z)
• Generative Deep Learning for Virtuosic Classical Music: Generative Adversarial Networks as Renowned Composers [0.0]
  Current AI-generated music lacks fundamental principles of good compositional techniques. We can create a better understanding of what parameters are necessary for a composition nearly indistinguishable from a master composition.
  arXiv  Detail & Related papers  (2021-01-01T05:40:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.