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. To overcome this inherent limitation, an enhanced adaptive notch filter with inverse model least mean square (ANF-IMLMS) algorithm is proposed as a basis of the AST system for vehicle powertrain response. Compared with the traditional FXLMS algorithm, the proposed algorithm not only increases the convergence speed, but also reduces the computational complexity. Thus, the overall performance of the AST system is improved. Numerical simulation applying measured powertrain data is conducted to validate the performance of the proposed system. Results demonstrate that the proposed system has improved spectral shaping capability with fast convergence.