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This paper describes an active sound tuning (AST) system for vehicle powertrain response. Instead of simply aiming to attenuate cabin interior noise, AST system is capable of reshaping the powertrain response based on predetermined vehicle sound quality criteria. However, conventional AST systems cannot yield a balanced result over the broad frequency range when applied to powertrain noise. It is due to the fact that existing systems are typically configured with the filtered-x least mean square (FXLMS) algorithm or its modified versions, which has inherent frequency dependent convergence behavior due to large dynamic range of secondary path (the electro-acoustic path from the control speaker to the error microphone). Therefore, fast convergence can only be reached at the resonant frequencies.

Active noise control systems have been gaining popularity in the last couple of decades, due to the deficiencies in passive noise abatement techniques. In the future, a novel combination of passive and active noise control techniques may be applied more widely, to better control the interior sound quality of vehicles. In order to maximize the effectiveness of this combined approach, smarter algorithms will be needed for active noise control systems. These algorithms will have to be computationally efficient, with high stability and convergence rates. This will be necessary in order to accurately predict and control the interior noise response of a vehicle. In this study, a critical review of the filtered-x least mean square (FXLMS) algorithm and several other newly proposed algorithms for the active control of vehicle powertrain noise, is performed. The analysis examines the salient features of each algorithm, and compares their system performance.