MusiChat: Vibe Composing for Music Creation

• URL: http://arxiv.org/abs/2607.24873v1
• Date: Mon, 27 Jul 2026 03:51:40 GMT
• Title: MusiChat: Vibe Composing for Music Creation
• Abstract summary: MusiChat is a conversational vibe composing system that enables collaborative human-AI music creation.<n>MusiChat integrates a large language model with a hybrid symbolic music engine through a memory-augmented architecture.<n>We evaluate MusiChat through objective analysis and human studies, achieving 95.31% and 100% accuracy for single- and multi-turn interactions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in AI music generation have enabled users to create complete musical pieces from natural-language prompts. However, most existing systems follow a prompt-and-regenerate paradigm, making iterative refinement difficult because users must repeatedly recreate compositions instead of directly evolving existing musical ideas. We present MusiChat, a conversational vibe composing system that enables collaborative human-AI music creation through natural-language interaction and iterative refinement. At the core of MusiChat is a hierarchical controllable music generation framework that separates lyric-aligned musical structure generation from expressive surface realization, allowing flexible stylistic transformations and structure-preserving edits. The system integrates a large language model with a hybrid symbolic music engine through a memory-augmented architecture that maintains the active composition state and user history across interactions. A hybrid intent-routing mechanism further enables efficient interpretation of both precise musical edits and open-ended creative requests. Rather than regenerating compositions from scratch, MusiChat incrementally transforms an evolving musical artifact while preserving relevant musical structure and user intent. We evaluate MusiChat through objective analysis and human studies, achieving 95.31% and 100% accuracy for single- and multi-turn interactions, respectively, and obtaining like-to-dislike ratios of 2:1 for melody naturalness and 3:1 for musical quality. Our results demonstrate that MusiChat supports coherent multi-turn music authoring and interactive human-AI co-creation through a conversational interface.

Related papers

• Co-policy: Responsive Human-Robot Co-Creation for Musical Performances [58.92814200971293]
  Co-policy is a framework for human-robot musical co-creation.<n>It separates semantic intent grounding, constrained musical variation, and visuomotor execution.<n>We show improved intent alignment, execution accuracy, and response frequency over diffusion-policy and ablated baselines.
  arXiv  Detail & Related papers  (2026-06-18T08:08:16Z)
• FIGMA: Towards FIne-Grained Music retrievAl [65.98380295254817]
  Retrieving music using natural language descriptions has improved with contrastive audio-text models such as CLAP, but current systems remain limited to coarse semantic queries.<n>We show that this limitation stems from the contrastive learning objective itself: despite being trained on long captions, CLAP-based models effectively utilize only the first few tokens.<n>We propose FIGMA, a multi-view contrastive architecture that addresses this limitation by jointly optimizing global audio-text alignment and frame-level, token-wise alignment.
  arXiv  Detail & Related papers  (2026-06-04T18:05:39Z)
• Music of Changing Lines: Toward a Culturally Situated Approach to the I-Ching [78.66052394412223]
  Music of Changing Lines re-centers the I-Ching as a meaning-bearing framework rather than a neutral randomizer.<n>Users perform Wen Wang Fa coin casting, which is accompanied in real time through probabilistic musical processes.<n>The resulting hexagrams and changing lines are interpreted by a large language model, Gemini, in relation to the user's inquiry.
  arXiv  Detail & Related papers  (2026-05-19T18:35:47Z)
• 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)
• The Ghost in the Keys: A Disklavier Demo for Human-AI Musical Co-Creativity [59.78509280246215]
  Aria-Duet is an interactive system facilitating a real-time musical duet between a human pianist and Aria, a state-of-the-art generative model.<n>We analyze the system's output from a musicological perspective, finding the model can maintain stylistic semantics and develop coherent phrasal ideas.
  arXiv  Detail & Related papers  (2025-11-03T15:26:01Z)
• ThinkSound: Chain-of-Thought Reasoning in Multimodal Large Language Models for Audio Generation and Editing [47.14083940177122]
  ThinkSound is a novel framework that enables stepwise, interactive audio generation and editing for videos.<n>Our approach decomposes the process into three complementary stages: semantically coherent, interactive object-centric refinement, and targeted editing.<n> Experiments demonstrate that ThinkSound achieves state-of-the-art performance in video-to-audio generation across both audio metrics and CoT metrics.
  arXiv  Detail & Related papers  (2025-06-26T16:32:06Z)
• ImprovNet -- Generating Controllable Musical Improvisations with Iterative Corruption Refinement [6.873190001575463]
  ImprovNet is a transformer-based architecture that generates expressive and controllable musical improvisations.<n>It can perform cross-genre and intra-genre improvisations, harmonize melodies with genre-specific styles, and execute short prompt continuation and infilling tasks.
  arXiv  Detail & Related papers  (2025-02-06T21:45:38Z)
• MuDiT & MuSiT: Alignment with Colloquial Expression in Description-to-Song Generation [18.181382408551574]
  We propose a novel task of Colloquial Description-to-Song Generation. It focuses on aligning the generated content with colloquial human expressions. This task is aimed at bridging the gap between colloquial language understanding and auditory expression within an AI model.
  arXiv  Detail & Related papers  (2024-07-03T15:12:36Z)
• MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models [57.47799823804519]
  We are inspired by how musicians compose music not just from a movie script, but also through visualizations. We propose MeLFusion, a model that can effectively use cues from a textual description and the corresponding image to synthesize music. Our exhaustive experimental evaluation suggests that adding visual information to the music synthesis pipeline significantly improves the quality of generated music.
  arXiv  Detail & Related papers  (2024-06-07T06:38:59Z)
• Controllable Lyrics-to-Melody Generation [14.15838552524433]
  We propose a controllable lyrics-to-melody generation network, ConL2M, which is able to generate realistic melodies from lyrics in user-desired musical style. Our work contains three main novelties: 1) To model the dependencies of music attributes cross multiple sequences, inter-branch memory fusion (Memofu) is proposed to enable information flow between multi-branch stacked LSTM architecture; 2) Reference style embedding (RSE) is proposed to improve the quality of generation as well as control the musical style of generated melodies; 3) Sequence-level statistical loss (SeqLoss) is proposed to help the model learn sequence-level
  arXiv  Detail & Related papers  (2023-06-05T06:14:08Z)
• Music Harmony Generation, through Deep Learning and Using a Multi-Objective Evolutionary Algorithm [0.0]
  This paper introduces a genetic multi-objective evolutionary optimization algorithm for the generation of polyphonic music. One of the goals is the rules and regulations of music, which, along with the other two goals, including the scores of music experts and ordinary listeners, fits the cycle of evolution to get the most optimal response. The results show that the proposed method is able to generate difficult and pleasant pieces with desired styles and lengths, along with harmonic sounds that follow the grammar while attracting the listener, at the same time.
  arXiv  Detail & Related papers  (2021-02-16T05:05:54Z)

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.