CompLex: Music Theory Lexicon Constructed by Autonomous Agents for Automatic Music Generation
This addresses the problem of manual effort in music generation tasks like algorithmic composition and style transfer for AI researchers and musicians, representing a novel method for a known bottleneck.
The paper tackles the limited availability of music data for AI-driven music generation by introducing CompLex, an automatically constructed music theory lexicon with 37,432 items, which improves performance across three state-of-the-art text-to-music generation models.
Generative artificial intelligence in music has made significant strides, yet it still falls short of the substantial achievements seen in natural language processing, primarily due to the limited availability of music data. Knowledge-informed approaches have been shown to enhance the performance of music generation models, even when only a few pieces of musical knowledge are integrated. This paper seeks to leverage comprehensive music theory in AI-driven music generation tasks, such as algorithmic composition and style transfer, which traditionally require significant manual effort with existing techniques. We introduce a novel automatic music lexicon construction model that generates a lexicon, named CompLex, comprising 37,432 items derived from just 9 manually input category keywords and 5 sentence prompt templates. A new multi-agent algorithm is proposed to automatically detect and mitigate hallucinations. CompLex demonstrates impressive performance improvements across three state-of-the-art text-to-music generation models, encompassing both symbolic and audio-based methods. Furthermore, we evaluate CompLex in terms of completeness, accuracy, non-redundancy, and executability, confirming that it possesses the key characteristics of an effective lexicon.