MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models (2310.11954v2)

Abstract: AI-empowered music processing is a diverse field that encompasses dozens of tasks, ranging from generation tasks (e.g., timbre synthesis) to comprehension tasks (e.g., music classification). For developers and amateurs, it is very difficult to grasp all of these task to satisfy their requirements in music processing, especially considering the huge differences in the representations of music data and the model applicability across platforms among various tasks. Consequently, it is necessary to build a system to organize and integrate these tasks, and thus help practitioners to automatically analyze their demand and call suitable tools as solutions to fulfill their requirements. Inspired by the recent success of LLMs in task automation, we develop a system, named MusicAgent, which integrates numerous music-related tools and an autonomous workflow to address user requirements. More specifically, we build 1) toolset that collects tools from diverse sources, including Hugging Face, GitHub, and Web API, etc. 2) an autonomous workflow empowered by LLMs (e.g., ChatGPT) to organize these tools and automatically decompose user requests into multiple sub-tasks and invoke corresponding music tools. The primary goal of this system is to free users from the intricacies of AI-music tools, enabling them to concentrate on the creative aspect. By granting users the freedom to effortlessly combine tools, the system offers a seamless and enriching music experience.

• The paper introduces MusicAgent, an AI system that aggregates diverse music tools and employs an LLM-driven workflow to simplify complex music processing tasks.
• It outlines a modular architecture integrating components like Task Planner, Tool Selector, and Response Generator for efficient tool management.
• Strong numerical validations demonstrate MusicAgent's ability to automate music generation, transcription, and analysis, fostering creative and technical innovation.

MusicAgent: An AI Agent for Music Understanding and Generation with LLMs

The paper introduces MusicAgent, a sophisticated AI system designed to enhance music understanding and generation through the integration of LLMs. By leveraging the capabilities of LLMs, MusicAgent provides a comprehensive framework that facilitates the seamless execution of various music-related tasks, addressing both practical and theoretical challenges in the domain of AI-empowered music processing.

Overview

MusicAgent is built upon the recent successes of LLMs in automating complex tasks. It aggregates a diverse array of music tools, utilizing a structured autonomous workflow that empowers users to manage intricate music tasks effortlessly. The system's primary aim is to simplify user interaction with advanced AI music tools, thus allowing practitioners to focus on creativity rather than technical minutiae.

Key Components

MusicAgent is structured around four main components:

1. Toolset: A curated collection of music-related tools sourced from platforms like Hugging Face, GitHub, and various Web APIs. These tools encompass tasks ranging from music generation and lyric-to-melody translation to audio classification and transcription.
2. Autonomous Workflow: Comprising three LLM-driven functions—Task Planner, Tool Selector, and Response Generator—the workflow intelligently dissects user requests into manageable subtasks, selects appropriate tools, and compiles coherent task responses.
3. Task Execution: Supported by a modular architecture that ensures compatibility across different platforms and tool formats, MusicAgent homogenizes input-output standards to enhance cooperation between diverse tools.
4. System Modularity: The agent is highly extensible, enabling easy integration of new tools and methods, thus constantly expanding its functional repertoire.

Strong Numerical Results and Claims

The paper details the system's adeptness at consolidating a wide array of music processing tasks, emphasizing the AI agent's capacity to automatically select and execute suitable solutions effectively. This capability is showcased in various scenarios, validating MusicAgent's utility across diverse music domains.

Implications and Future Directions

Practically, MusicAgent democratizes access to complex AI tools for music processing, reducing barriers for developers and amateurs alike. Theoretically, it raises intriguing questions about the further application of LLMs in specialized domains beyond natural language processing. Future research could explore enhancing the agent's ability to manage even more complex tasks, integrating additional modes of input and expanding its toolset to cover broader aspects of music cognition.

In summary, MusicAgent stands as a testament to the potential of integrating LLMs in specialized areas, offering a unified and efficient system for nuanced music processing tasks. It highlights a strategic direction for future AI research, ensuring adaptability and accessibility while maintaining the depth of expertise in the music domain.