CompLex: Music Theory Lexicon Constructed by Autonomous Agents for Automatic Music Generation

• URL: http://arxiv.org/abs/2508.19603v1
• Date: Wed, 27 Aug 2025 06:36:49 GMT
• Title: CompLex: Music Theory Lexicon Constructed by Autonomous Agents for Automatic Music Generation
• Authors: Zhejing Hu, Yan Liu, Gong Chen, Bruce X. B. Yu,
• Abstract summary: This paper seeks to leverage comprehensive music theory in AI-driven music generation tasks, such as algorithmic composition and style transfer.<n>We introduce a novel automatic music lexicon construction model that generates a lexicon, named CompLex, comprising 37,432 items.<n> CompLex demonstrates impressive performance improvements across three state-of-the-art text-to-music generation models.
• Score: 12.545590455588856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Scalable Music Cover Retrieval Using Lyrics-Aligned Audio Embeddings [7.3788020779010255]
  Music Cover Retrieval, also known as Version Identification, aims to recognize distinct renditions of the same underlying musical work.<n>We introduce LIVI (Lyrics-Informed Version Identification), an approach that seeks to balance retrieval accuracy with computational efficiency.
  arXiv  Detail & Related papers  (2026-01-16T13:11:38Z)
• Music Flamingo: Scaling Music Understanding in Audio Language Models [98.94537017112704]
  Music Flamingo is a novel large audio-language model designed to advance music understanding in foundational audio models.<n> MF-Skills is a dataset labeled through a multi-stage pipeline that yields rich captions and question-answer pairs covering harmony, structure, timbre, lyrics, and cultural context.<n>We introduce a post-training recipe: we first cold-start with MF-Think, a novel chain-of-thought dataset grounded in music theory, followed by GRPO-based reinforcement learning with custom rewards.
  arXiv  Detail & Related papers  (2025-11-13T13:21:09Z)
• Discovering "Words" in Music: Unsupervised Learning of Compositional Sparse Code for Symbolic Music [50.87225308217594]
  This paper presents an unsupervised machine learning algorithm that identifies recurring patterns -- referred to as music-words'' -- from symbolic music data.<n>We formulate the task of music-word discovery as a statistical optimization problem and propose a two-stage Expectation-Maximization (EM)-based learning framework.
  arXiv  Detail & Related papers  (2025-09-29T11:10:57Z)
• Decoding Musical Origins: Distinguishing Human and AI Composers [0.6246322794612152]
  YNote is a novel, machine-learning-friendly music notation system.<n>We train an effective classification model capable of distinguishing whether a piece of music was composed by a human.<n>The model achieves an accuracy of 98.25%, successfully demonstrating that YNote retains sufficient stylistic information.
  arXiv  Detail & Related papers  (2025-09-14T17:50:33Z)
• Towards an AI Musician: Synthesizing Sheet Music Problems for Musical Reasoning [69.78158549955384]
  We introduce a novel approach that treats core music theory rules, such as those governing beats and intervals, as programmatic functions.<n>This approach generates verifiable sheet music questions in both textual and visual modalities.<n> Evaluation results on SSMR-Bench highlight the key role reasoning plays in interpreting sheet music.
  arXiv  Detail & Related papers  (2025-09-04T09:42:17Z)
• Evaluation of pretrained language models on music understanding [0.0]
  We demonstrate that Large Language Models (LLM) suffer from 1) prompt sensitivity, 2) inability to model negation, and 3) sensitivity towards the presence of specific words. We quantified these properties as a triplet-based accuracy, evaluating the ability to model the relative similarity of labels in a hierarchical ontology. Despite the relatively high accuracy reported, inconsistencies are evident in all six models, suggesting that off-the-shelf LLMs need adaptation to music before use.
  arXiv  Detail & Related papers  (2024-09-17T14:44:49Z)
• Instruct-MusicGen: Unlocking Text-to-Music Editing for Music Language Models via Instruction Tuning [24.6866990804501]
  Instruct-MusicGen is a novel approach that finetunes a pretrained MusicGen model to efficiently follow editing instructions.<n>Remarkably, Instruct-MusicGen only introduces 8% new parameters to the original MusicGen model and only trains for 5K steps.
  arXiv  Detail & Related papers  (2024-05-28T17:27:20Z)
• 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)
• MuPT: A Generative Symbolic Music Pretrained Transformer [56.09299510129221]
  We explore the application of Large Language Models (LLMs) to the pre-training of music. To address the challenges associated with misaligned measures from different tracks during generation, we propose a Synchronized Multi-Track ABC Notation (SMT-ABC Notation) Our contributions include a series of models capable of handling up to 8192 tokens, covering 90% of the symbolic music data in our training set.
  arXiv  Detail & Related papers  (2024-04-09T15:35:52Z)
• Simple and Controllable Music Generation [94.61958781346176]
  MusicGen is a single Language Model (LM) that operates over several streams of compressed discrete music representation, i.e., tokens. Unlike prior work, MusicGen is comprised of a single-stage transformer LM together with efficient token interleaving patterns.
  arXiv  Detail & Related papers  (2023-06-08T15:31:05Z)
• MusCaps: Generating Captions for Music Audio [14.335950077921435]
  We present the first music audio captioning model, MusCaps, consisting of an encoder-decoder with temporal attention. Our method combines convolutional and recurrent neural network architectures to jointly process audio-text inputs. Our model represents a shift away from classification-based music description and combines tasks requiring both auditory and linguistic understanding.
  arXiv  Detail & Related papers  (2021-04-24T16:34:47Z)

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.