RPPNet: Perceptually-Grouped Rhythm-Pitch Primitives for Long-Term Structure Melody Generation via Boundary-Aware Modeling
Abstract
Existing symbolic music generation models typically use bars as the basic structural unit. However, human perception of musical phrases often does not align with notated bar lines, leading to long-term structural fragmentation. This paper proposes RPPNet-a two-stage deep learning architecture with variable structural boundaries. It first generates variable-length Rhythm-Pitch Primitive (RPP) sequences, where each RPP encodes note count, rhythm, and contour; then decodes the RPP sequences into concrete notes. The grouping of RPPs is automatically derived from acoustic cues, auditory inertia, and similarity perception based on music psychology. Experiments show that melodies generated by RPPNet are superior in both long-term structure and musicality, with significant improvements across all subjective evaluation dimensions. Ablation studies confirm that the performance gain stems from the structural correctness of the psychological representation, rather than from model capacity. This work offers an interdisciplinary perspective for music generation, integrating music theory, computational modeling, and music psychology.
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
[RPPNet introduces a perceptually-grounded, variable-length structural representation for symbolic music generation, demonstrating that aligning model boundaries with human auditory grouping principles significantly improves long-term structural coherence compared to fixed bar-level approaches.]
Methodology
The paper proposes RPPNet, a hierarchical two-stage architecture for symbolic melody generation. The core innovation lies in replacing fixed bar-level tokenization with perceptually-grouped Rhythm-Pitch Primitives (RPPs). The authors employ a heuristic algorithm based on music psychology (acoustic cues, auditory inertia, similarity perception) to segment melodies into variable-length RPPs. The generation process is decoupled: an RPP-level Transformer generates the structural skeleton, which is then instantiated into note-level events via a time-scale expansion mapping. The methodology is theoretically grounded in cognitive musicology, offering a novel perspective on structural segmentation that challenges the dominant fixed-grid assumptions in models like Museformer and MELONS. The decoupled serial prediction mechanism at the RPP level is a technically sound approach to managing the complexity of long-sequence generation.
Results & evaluation
The experimental setup utilizes the MelodyNet dataset. The evaluation relies heavily on subjective listening tests (MOS-style) with 15 participants, which is a standard but resource-intensive method for music generation. RPPNet significantly outperforms baselines (Museformer, MELONS) in subjective metrics, particularly in "Structure" and "Overall Impression." The inclusion of ablation studies (Random-Grouped vs. Heuristic-Grouped) is crucial and effectively isolates the contribution of the psychological grouping algorithm from mere variable-length tokenization. However, the objective metrics are limited; the paper notes that PPL and Structural Error are difficult to compare across different tokenization schemes, leading to a reliance on subjective scores. The gap between generated music and human composition (6.69 vs 7.47) remains significant, though the paper attributes this to rhythmic distribution mismatches.
Reproducibility
The paper provides a GitHub repository link, which is a positive step for reproducibility. The heuristic algorithm for RPP grouping is described, but the full formalization of the weight function $W()$ is stated to be in the repository, which may contain implementation-specific details not fully explicit in the text. The dataset (MelodyNet) is public. The subjective evaluation protocol is described, but the small sample size (15 participants) limits the statistical power of the findings, although significance tests are provided.
Limitations
The primary limitation is the reliance on a heuristic, rule-based algorithm for RPP segmentation, which may not generalize well to all musical styles or complex polyphonic textures. The current model is limited to monophonic melody generation. The subjective evaluation, while showing statistical significance, suffers from a small participant pool and potential bias. The objective metrics do not fully capture the perceptual improvements claimed, as standard metrics like PPL are not directly comparable across different tokenization granularities.
Broader impact
This work bridges the gap between computational music generation and music psychology/cognition. By demonstrating that perceptually-grounded structural units lead to more coherent music, it offers a new paradigm for designing generative models that align with human auditory perception. This could influence future research in hierarchical modeling and structured generation beyond music, such as in language or video, where fixed-grid assumptions may also be limiting. [RPPNet introduces a perceptually-grounded, variable-length structural representation for symbolic music generation, demonstrating that aligning model boundaries with human auditory grouping principles significantly improves long-term structural coherence compared to fixed bar-level approaches.]
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