【摘要】 针对音频合成音色自然度不足的问题，本研究提出一种融合特征分析与智能优化的多阶段框架。首先，通过合成三类声波构建数据集，提取声学特征，利用降维与聚类量化合成音与自然音的差异；其次，基于贝叶斯优化自适应分配特征权重，筛选关键区分性指标；最后，结合强化学习动态调整合成参数，以聚类中心距离为奖励驱动音色逼近自然声分布。实验证明，本方法显著提升合成音色的自然度，为数据驱动的音频优化提供了高效解决方案。更多还原

【Abstract】 To address the insufficient naturalness of timbre in audio synthesis, this study proposes a multi-stage framework integrating feature analysis and intelligent optimization. First, a dataset is constructed by synthesizing three types of sound waves, from which acoustic features are extracted. Dimensionality reduction and clustering are employed to quantify the differences between synthetic and natural sounds. Second, Bayesian optimization is applied to adaptively assign feature weights, identifying key discriminative indicators. Finally, reinforcement learning dynamically adjusts synthesis parameters(e.g., frequency, decay factor) using clustering center distance as a reward to drive synthetic timbre closer to natural sound distributions. Experimental results demonstrate that this method significantly enhances the naturalness of synthesized timbre, providing an efficient data-driven solution for audio optimization.更多还原