Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations
arXiv:2603.07584v2 Announce Type: replace-cross Abstract: 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
The increasing sophistication of AI models and the critical need for vast, high-quality datasets for training are driving innovation in synthetic data generation methods.
This development addresses a fundamental bottleneck in data acquisition for specific AI applications, reducing costs and accelerating development in areas reliant on complex audio modeling.
The ability to procedurally generate high-fidelity, annotated datasets for engine sounds removes a major barrier to entry and innovation in automotive audio AI and related fields.
- · Automotive industry (R&D)
- · AI/ML audio developers
- · Simulation and virtual prototyping companies
- · Active sound design firms
- · Companies relying solely on traditional, expensive data collection
- · Specialized audio measurement equipment providers (limited impact)
Rapid advancement in AI-driven automotive sound design and virtual prototyping becomes possible.
The methodology could be adapted to generate synthetic data for other complex acoustic environments beyond engines, expanding AI application domains.
Ethical considerations around distinguishing synthetic audio from real recordings may emerge as generation quality approaches indistinguishable levels.
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Read at arXiv cs.LG