Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations

TL;DR

This paper introduces a novel analysis-driven method for generating a large, annotated engine sound dataset using spectral analysis and synthesis, facilitating research in engine sound modeling and neural synthesis.

Contribution

It presents a new framework for creating a large, annotated engine sound dataset with sample-accurate control parameters, combining real data analysis and synthesis techniques.

Findings

Generated dataset covers diverse operating conditions and harmonic profiles.

Synthesized sounds preserve characteristic harmonic structures.

Dataset is validated for use in learning-based engine sound tasks.

Abstract

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.