An efficient technique for modeling and synthesis of automotive engine sounds

This paper presents a new method for modeling and synthesis of automotive engine sounds using a deterministic-stochastic signal decomposition approach. First, the deterministic component is extracted using a synchronous discrete Fourier transform method and this is subtracted out from the original signal. Next, the (residual) stochastic component is modeled (and synthesized) using a new multipulse excited time-series modeling technique. The effectiveness of the proposed methodology is demonstrated using recorded data sets of actual engine sounds. The results of both numerical and subjective assessment tests are presented.