MusicLayout: Explicit Structural Planning for Controllable Text-to-Music Generation
Abstract
Text-to-music generation has advanced rapidly, but current systems still rely primarily on global text prompts, leaving the structural organization of generated music implicit and difficult to inspect, control, or revise before audio generation. To address this issue, we introduce MusicLayout, an explicit intermediate representation for controlling musical structure in text-to-music generation. MusicLayout describes a musical piece as a time-aligned layout of sections, textures, repetitions, variations, and instrument-level arrangements, serving as an interpretable planning layer between textual intent and the generated music. We integrate MusicLayout into a text-to-music framework built on a unified autoregressive formulation, where the model first generates a MusicLayout representation and subsequently predicts audio tokens conditioned on this representation within a single sequence. The resulting MusicLayout can be inspected and modified prior to audio generation, providing a mechanism for layout-level structural control. We evaluate MusicLayout through layout-conditioned generation, layout manipulation experiments, and matched-data ablations, providing evidence that explicit layout planning can improve long-range structural organization and support layout-level control.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
MusicLayout introduces an explicit, time-aligned intermediate representation for controlling musical structure in text-to-music generation, enabling interpretable planning and layout-level control within a unified autoregressive framework.
Methodology
The paper proposes MusicLayout, an explicit intermediate representation for text-to-music generation. The core methodological contribution is the design of a structured, time-aligned token sequence that describes musical sections, textures, variations, and instrument arrangements. This layout is generated by a unified autoregressive language model (based on ACE-Step 1.5) before audio tokens are predicted. The approach effectively introduces a "planning" layer, analogous to spatial layout planning in image generation, but adapted for the temporal domain of music. The serialization grammar is well-defined, and the integration into the autoregressive sequence is straightforward. However, the novelty is somewhat incremental; it applies a known paradigm (explicit intermediate representation) to a new modality (music) without introducing fundamentally new algorithmic mechanisms for the generation process itself. The reliance on MIDI-synthesized audio for training the layout structure is a significant methodological constraint, as it decouples the structural planning from the acoustic fidelity of the final output.
Results & evaluation
The evaluation is comprehensive, covering objective metrics (FAD, CLAPScore, PaSST-KL, SSIM, SCM Energy Distance, boundary agreement) and subjective human evaluation. The authors include strong baselines (MusicGen, ACE-Step, Stable Audio) and, crucially, matched-data controls (finetuned ACE-Step without layout, shuffled layout training/inference) to isolate the effect of the explicit planning. The results show that MusicLayout improves structural organization (boundary agreement) and can be manipulated to control structure. However, the acoustic quality metrics (FAD, CLAP) are generally inferior to or comparable with baselines, which the authors attribute to the use of MIDI-synthesized training data. The evaluation on MuChin (real audio) shows a performance drop, highlighting the domain gap. The subjective evaluation supports the claim of improved structural control and text consistency for experienced listeners. The ablation studies are rigorous and help validate the specific contribution of the layout tokens.
Reproducibility
The paper provides detailed implementation details, including the model architecture (ACE-Step 1.5 backbone), training settings (batch size, learning rate, GPU count), and the specific datasets used (FreeMIDI, MidiCaps, MuChin). The annotation pipeline for creating MusicLayout from MIDI is described in an algorithmic format. The code for the layout extraction and the model adaptation is not explicitly linked, but the description is sufficient for a competent researcher to reproduce the method. The use of open-source components (FluidSynth, PrettyMIDI, ACE-Step) aids reproducibility.
Limitations
The primary limitation is the reliance on MIDI-synthesized audio for training the structural planner. This creates a domain gap when evaluating on real audio (MuChin), where performance degrades. The system cannot edit existing audio or regenerate selected regions; it only allows pre-synthesis layout manipulation. The vocabulary for sections, textures, and instruments is closed and may not cover all musical styles or nuances. The quality of the generated layout depends on the text prompt, and if the prompt is ambiguous, the generated layout may not align with the user's intent. The acoustic fidelity is limited by the quality of the MIDI synthesis used in training, although the DiT renderer is frozen and presumably capable of higher quality if trained on better data.
Broader impact
This work contributes to the field of controllable generative AI by demonstrating the value of explicit intermediate representations for complex, structured data like music. It opens up new avenues for human-in-the-loop music creation, where users can plan the structure of a piece before generation. This could lower the barrier to entry for music production and enable more precise artistic control. However, the reliance on MIDI data and the potential for generating low-quality audio from poor MIDI synthesis are practical concerns. The work also raises questions about the generalizability of such explicit planning methods to other domains. MusicLayout introduces an explicit, time-aligned intermediate representation for controlling musical structure in text-to-music generation, enabling interpretable planning and layout-level control within a unified autoregressive framework.
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