MuLaCover: Flexible Cover Song Generation via Symbolic Tonal Control
Abstract
A cover song re-renders an existing piece, most commonly by preserving its tonal content -- melody and chord progression -- while reshaping other musical attributes. This suggests a natural formulation for generative cover synthesis with pretrained music language models: explicitly control tonal content, while specifying lyrics and style through text. Existing approaches condition on frame-level features, tightly coupling generation to the reference's timing and structure, and limiting flexibility such as tempo variation, structural rearrangement, or partial conditioning. To this end, we present MuLaCover, a cover generation model that conditions a pretrained text-to-song foundation model on an editable symbolic lead sheet. Our design enables alignment-free control: generated outputs preserve the tonal signature of the source without frame-level alignment, allowing flexible timing, structure, and segment-level generation. We further introduce a training curriculum with partially mismatched audio-symbolic pairs, improving diversity and robustness. We evaluate MuLaCover with objective metrics, standard subjective ratings, and in-depth expert interviews that provide fine-grained diagnostic insights. Experiments show that MuLaCover achieves state-of-the-art performance among open-source systems and is competitive with leading commercial models such as Suno-v5.5. Audio samples and code are available at our project page: https://mulalabs.ai/mulacover/
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
MuLaCover introduces a flexible cover song generation framework by conditioning a text-to-song model on BPM-normalized symbolic lead sheets via gated cross-attention. The paper demonstrates that decoupling tonal content from timing through symbolic representation and cross-cover training enables superior controllability and diversity compared to frame-level conditioning, achieving performance competitive with leading commercial systems.
Methodology
The paper proposes MuLaCover, a system for cover song generation that conditions a pretrained text-to-song foundation model (HeartMuLa) on a symbolic lead sheet (melody + chords) rather than frame-level audio features. The core methodological contribution is the use of BPM-normalized symbolic representations to decouple tonal content from timing, enabling flexible tempo and structural changes. The model injects this condition via a Gated Adaptive Cross-Attention (GACA) module into the last layers of the backbone. A key innovation is the three-stage training curriculum, specifically the "cross-cover" finetuning stage where tonal content from one rendition is paired with lyrics/style from another, effectively teaching the model to disentangle content from style. The ablation studies rigorously validate the necessity of BPM normalization, the gate mechanism, and the cross-cover training strategy.
Results & evaluation
The evaluation is comprehensive, utilizing objective metrics (AudioBox-Aesthetics, SongEval, PER, MuQ similarity, CSI accuracy) and subjective evaluations (listening study with 22 participants, expert interviews). The paper compares against strong baselines including open-source systems (SongEcho, ACE-Step-1.5) and a leading commercial model (Suno-v5.5). Results show MuLaCover achieves state-of-the-art performance among open-source systems and is competitive with Suno-v5.5. The partial-reference cover task (inpainting/outpainting) is also evaluated, demonstrating the flexibility of the alignment-free approach. The expert interview protocol provides qualitative insights into why certain systems perform better on specific musical attributes.
Reproducibility
The paper provides detailed descriptions of the architecture, training stages, and data pipeline. It mentions the use of specific models for transcription (YourMT3, etc.) and data synthesis. While code is stated to be available at the project page, the reliance on synthetic data from commercial providers and specific internal pipelines (like the cross-cover pairing logic) might limit full external reproduction without access to those specific data assets. However, the methodological details are sufficient for implementation.
Limitations
The system relies on the quality of the symbolic transcription models for melody and chords; errors in transcription will propagate to the generation. The training data is synthetic, which may limit the model's ability to handle the nuances of human-recorded covers with complex timbral variations. The "cover" definition is fixed at the lead-sheet level, which may not capture all artistic interpretations of a cover (e.g., jazz re-harmonization).
Broader impact
This work advances controllable music generation by providing a flexible, alignment-free method for conditioning on musical structure. It bridges the gap between symbolic music representation and audio generation, offering a practical tool for music production and co-creation. The approach could be extended to other domains where structural control is needed without strict temporal alignment. MuLaCover introduces a flexible cover song generation framework by conditioning a text-to-song model on BPM-normalized symbolic lead sheets via gated cross-attention. The paper demonstrates that decoupling tonal content from timing through symbolic representation and cross-cover training enables superior controllability and diversity compared to frame-level conditioning, achieving performance competitive with leading commercial systems.
Author of this paper? Send a correction.