Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations
This work provides a valuable, publicly available dataset for researchers and engineers in the automotive audio industry, particularly for those working on active sound design, virtual prototyping, and data-driven engine sound synthesis, by overcoming the challenges of obtaining real-world data.
This paper addresses the scarcity of high-quality engine sound data by proposing an analysis-driven framework to procedurally generate engine audio with precise control annotations. They created the Procedural Engine Sounds Dataset, comprising 19 hours across 5,935 files, which was validated to preserve characteristic harmonic structures compared to real recordings.
Computational engine sound modeling is central to the automotive audio industry, particularly for active sound design, virtual prototyping, and emerging data-driven engine sound synthesis methods. These applications 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 generate the Procedural Engine Sounds Dataset (19 hours, 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 baseline experiments confirm its suitability for learning-based parameter estimation and synthesis tasks. The dataset is released publicly to support research on engine timbre analysis, control parameter estimation, acoustic modeling and neural generative networks.