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文章背景与核心概要

近年来，可控音乐生成技术多集中于自回归模型，而基于扩散系统的音乐生成相对较少受到探索。本文针对 Stable Audio Open 等潜在扩散音乐生成模型，提出了一种轻量级、无需训练（training-free）的音高控制方法。通过利用成对的音频和 MIDI 数据，作者在模型的变分自编码器（VAE）潜空间上训练了一个约 12.5 万参数的小型卷积探针。

在推理阶段，这个被冻结的探针充当可微分损失函数，通过其对去噪潜变量的梯度，将生成过程引导至用户指定的旋律，且无需对基础模型进行任何架构修改或重新训练。实验评估表明，与无引导的基线相比，基于探针引导的生成方法使旋律连贯性提升了 2.4 倍，这证明了在扩散模型的音乐潜空间中，具有音乐意义的结构是完全可以被恢复并进行有效引导的。

Pitch-class Steering for Diffusion-based Music Generation via Latent-space Probes

Summary

This paper introduces a lightweight, training-free method for controlling pitch content in diffusion-based music generation models like Stable Audio Open. By training a small convolutional probe (~125k parameters) on the model's variational autoencoder (VAE) latent space using paired audio and MIDI data, the authors enable precise pitch steering at inference time.

Operating as a differentiable loss function, the frozen probe guides the denoising latent toward a user-specified melody without requiring any architectural alterations or retraining of the base model. Experimental evaluations demonstrate a 2.4x increase in melodic coherence compared to unguided baselines, proving that meaningful musical structure can be effectively steered within diffusion latent spaces.

Metadata & Document Information

  • arXiv ID: arXiv:2609.04516 [cs.SD]
  • Subjects: Sound (cs.SD); Artificial Intelligence (cs.AI)
  • Authors: Yushi Ye, Wilson Zheng, Yongyi Zang
  • Publication: Accepted at IEEE MLSP 2026 (6 pages, 3 figures, 3 tables)
  • Submitted: September 3, 2026
  • DOI: 10.48550/arXiv.2609.04516

Abstract

Recent work on controllable music generation has focused on autoregressive models, leaving diffusion-based systems comparatively underexplored. We present a lightweight method for steering the pitch content of audio produced by Stable Audio Open, a latent diffusion model for music synthesis. A small convolutional probe containing approximately 125k parameters is trained to decode frame-level pitch-class activations from the model's variational autoencoder latent space, using paired audio and MIDI data. At inference time, the frozen probe serves as a differentiable loss function: its gradient with respect to the denoising latent is used to nudge generation toward a user-specified pitch-class sequence, requiring no retraining or architectural modification of the base model. Across 27 evaluation trials spanning 9 text prompts and 3 target melodies, probe-guided generation increases melodic coherence by 2.4x over the unguided baseline (\(p < 1e-5\), Wilcoxon signed-rank test), demonstrating that musically meaningful structure is both recoverable and steerable in diffusion-based music latent spaces.

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