SegTune: Structured and Fine-Grained Control for Song Generation
Abstract
Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts. However, most systems fail to model temporally varying attributes of songs, severely limiting fine-grained control over musical structure and dynamics. To address this, we propose SegTune, a Diffusion Transformer-based framework enabling structured and fine-grained controllability by allowing users or large language models (LLMs) to specify local musical descriptions aligned to song segments. These segment prompts are temporally broadcast to corresponding time windows, while global prompts ensure stylistic coherence. To support precise lyric-to-music alignment, we introduce an LLM-based duration predictor that autoregressively generates sentence-level timestamps in LyRiCs format. We further construct a large-scale data pipeline for high-quality song collection with aligned lyrics and prompts, and propose new metrics to evaluate segment alignment and vocal consistency. Experiments demonstrate that SegTune outperforms existing baselines in both musicality and controllability. Visit our project page (https://github.com/KlingAIResearch/SegTune) for codes and more generated songs.
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
The main contribution of this paper is the introduction of SegTune, a novel framework for structured and fine-grained control in song generation, which significantly enhances the controllability and musical quality of generated audio. This work represents a meaningful step forward in the field of machine learning for audio, addressing long-standing challenges in song generation and setting a new standard for future research in this area.
Methodology
The methodology presented in SegTune is innovative, leveraging a Diffusion Transformer-based framework that introduces a hierarchical conditioning paradigm for song generation. This approach allows for fine-grained control over musical attributes by utilizing both global and segment-level prompts, which is a significant advancement over existing models that primarily rely on global prompts. The introduction of an LLM-based duration predictor to generate accurate timestamps for lyrics is particularly noteworthy, as it addresses a common challenge in song generation related to lyric alignment without requiring manual annotations. The architecture is well-structured, and the integration of segment encoders enhances the model's ability to capture temporal dynamics in music.
Results & evaluation
The experimental evaluation is robust, featuring a comprehensive set of objective metrics (e.g., Phoneme Error Rate, Audiobox-Aesthetics, SongEval) and subjective assessments (Mean Opinion Score). The authors compare SegTune against several state-of-the-art baselines, demonstrating clear improvements in both musicality and controllability. The experiments are well-designed, with a large dataset and multiple evaluation criteria, providing a thorough assessment of the model's performance. The results indicate that SegTune outperforms existing models, particularly in terms of instruction-following capabilities and audio quality.
Reproducibility
The paper provides sufficient details regarding the model architecture, training procedures, and evaluation metrics, which would facilitate reproducibility. The authors describe their data pipeline and the processes used for training and fine-tuning the model. However, the reliance on proprietary datasets and specific model configurations may pose challenges for full reproducibility outside their environment.
Limitations
The authors acknowledge several limitations, including the model's dependence on clearly defined segment structures during training, which may affect performance when user inputs are ambiguous. Additionally, while the hierarchical prompting allows for segment-level control, the model does not address fine-grained intra-segment dynamics, such as gradual crescendos. These limitations highlight areas for future research and improvement.
Broader impact
The development of SegTune has significant implications for democratizing music creation, enabling users with varying levels of expertise to generate high-quality songs. This could lead to increased participation in music production and innovation in creative workflows. However, it also raises concerns about the potential impact on professional musicians and the music industry, necessitating careful consideration of copyright and ethical issues related to AI-generated content. The main contribution of this paper is the introduction of SegTune, a novel framework for structured and fine-grained control in song generation, which significantly enhances the controllability and musical quality of generated audio. This work represents a meaningful step forward in the field of machine learning for audio, addressing long-standing challenges in song generation and setting a new standard for future research in this area.
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