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An Agent-Based Framework for Automated Higher-Voice Harmony Generation

Created by
  • Haebom

Author

Nia D'Souza Ganapathy, Arul Selvamani Shaja

Outline

This paper presents the "Agentic AI-enabled Higher Harmony Music Generator," an agent-based AI system that aims to generate musically consistent and aesthetically pleasing harmonies. The system consists of a Music Ingestion Agent, which processes and normalizes input music; a Chord Knowledge Agent (based on Transformer) that interprets complex chord symbols; a Harmony Generation Agent (using Harmony-GPT and Rhythm-Net) that generates melodically and rhythmically complementary harmonic lines; and an Audio Production Agent (based on GANs) that renders the final symbol output as high-quality audio. By delegating specific tasks to specialized agents, the system mimics the collaborative process of human musicians, enabling robust data processing, deep theoretical understanding, creative composition, and realistic audio synthesis.

Takeaways, Limitations

Takeaways:
A modular architecture leveraging expert agents that mimics the collaborative process of human musicians, enabling more sophisticated harmonic generation.
Improve the performance of each agent by utilizing various deep learning models (Transformer, RNN, GAN).
Enhance the realism of your final results through high-quality audio synthesis.
Limitations:
There is no specific mention of Limitations in the paper itself (based solely on the information presented in the Abstract).
An objective evaluation of the performance and creativity of real systems is needed.
Further analysis of the interaction and collaboration efficiency between each agent is needed.
Possible restrictions on specific music genres or styles may need to be reviewed.
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