Computational engine sound modeling is central to the automotive audio industry, particularly for active sound design applications and virtual prototyping. Emerging data-driven engine sound synthesis methods require large volumes of standardized, clean audio recordings with precisely time-aligned operating-state annotations: data that is difficult to obtain due to high costs, specialized measurement equipment requirements, and inevitable noise contamination. We present an analysis-driven framework for generating engine audio with sample-accurate control annotations. The method extracts harmonic structures from real recordings through pitch-adaptive spectral analysis, which then drive an extended parametric harmonic-plus-noise synthesizer. With this framework, we augment 5-10 min of source audio per engine 15-30x via diverse control trajectories and parametric variation, producing the Procedural Engine Sounds Dataset (19.0 h, 5,935 files): a set of engine audio signals with sample-accurate RPM and torque annotations spanning a wide range of operating conditions, signal complexities, and harmonic profiles. Comparison against real recordings validates that the synthesized data preserves characteristic harmonic structures, and a baseline differentiable synthesis network trained on the dataset confirms its suitability for data-driven engine sound modeling. The dataset is released publicly to support research on engine timbre analysis, control parameter estimation, and neural generative synthesis.

翻译：计算引擎声音建模是汽车音频行业的核心技术，尤其在主动声音设计应用和虚拟原型开发中至关重要。新兴的数据驱动引擎声音合成方法需要大量标准化、干净的音频记录，并配备精确时间对齐的运行状态标注——由于高昂成本、专业测量设备要求及不可避免的噪声污染，这类数据难以获取。我们提出一种分析驱动框架，用于生成具有样本级精确控制标注的引擎音频。该方法通过音高自适应频谱分析从真实录音中提取谐波结构，进而驱动扩展参数化谐波加噪声合成器。利用该框架，我们通过多样化的控制轨迹和参数变化，将每个引擎的5-10分钟源音频扩充15-30倍，生成程序化引擎声音数据集（19.0小时，5,935个文件）：包含样本级精确转速和扭矩标注的引擎音频信号集，覆盖广泛运行条件、信号复杂度和谐波轮廓。与真实录音的对比验证了合成数据保留了特征谐波结构，而在该数据集上训练的基线可微合成网络证实了其对数据驱动引擎声音建模的适用性。该数据集已公开发布，以支持引擎音色分析、控制参数估计和神经生成合成等研究方向。