Researchers have developed a novel framework for procedurally generating engine sounds with precise control annotations, addressing the difficulty of obtaining such data for automotive audio applications. This method extracts harmonic structures from real recordings to drive a parametric synthesizer, augmenting limited source audio to create a large dataset of engine sounds with sample-accurate RPM and torque annotations. The resulting dataset, comprising 19 hours of audio, has been validated against real recordings and demonstrated its utility in training a synthesis network, making it publicly available to advance research in engine sound analysis and modeling. AI
RANK_REASON This is a research paper detailing a new method and dataset for engine sound synthesis. [lever_c_demoted from research: ic=1 ai=0.7]
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