## 1 Introduction
[12] introduced an improved objective quality assessment method that can be used for mono signals and multichannel audio signals considering both \"spatial\" and \"timbre\" factors. [PERSON] et al. [13] proposed a new method to design and generate targeted interior sounds to achieve the desired \"powerful\" and \"pleasant\" sound quality in the cabin, and developed an intensity index and a pleasantness index to evaluate interior sound perception. [PERSON] et al. [14] proposed a new sound quality objective evaluation (SQOE) method, compared the performance of the proposed SQOE method with ArtemiS software, and verified the subjective and objective correlation analysis. [PERSON] et al. [15] proposed different algorithms for Adaptive Feedback Cancellation (AFC) for perceptual audio quality assessment, including speech and music signals. [PERSON] et al. [16] proposed a new objective sound quality evaluation method for in-vehicle noise based on the displacement of the basement membrane of the human ear, and established two BP neural network models for in-vehicle noise sound quality evaluation. The results show that the new metric called the statistical mean values of the basilar membrane displacement responses (SMVBMDR) can be used as an effective physio-acoustic metric to objectively evaluate the sound quality of vehicle noise. Vehicle powertrains are changing as technology develops. Creating an artificial engine sound with an additional audio system will make the vehicle sportier and more active. [PERSON] et al. [17] proposed an Active Noise Control system with a modified FxLMS algorithm approach using Digital Adaptive Filter which is a stable and fast convergent in a real-time changing environment to minimize the interior noise inside the car cabin. [PERSON] et al. [18] introduced an acoustic spectrum analysis method based on an auditory model and convolutional neural network (CNN) model to construct an automatic noise quality classification model. The results show that the input evaluation score based on the auditory spectrum in the CNN classification model was more accurate than the short-time average energy input evaluation score. [PERSON] et al. [19] proposed a sound quality prediction method based on particle swarm optimization (PSO) back-propagation neural network (BPNN). The experimental results show that the PSO-BPNN method can achieve faster convergence, improve the accuracy of sound quality prediction, and further lay a foundation for the control of in-vehicle sound quality. [PERSON] et al. [20] proposed a sound quality evaluation model based on complementary ensemble empirical mode decomposition (CEEMD), Hilbert transform (HT) and relevance vector machine (RVM) to predict the sound quality of acoustical signals at power coupling mechanism of HEV. [PERSON] et al. [21] proposed a new adaptive algorithm for active sound design (ASD). The algorithm will make the engine sound up to the specific noise level planned by the sound designer. [PERSON] et al. [22] proposed an active wideband sound quality control algorithm based on the traditional wideband active noise equalizer (ANE). The algorithm can shape the spectrum of the residual noise according to the sound quality filter, and can accurately control the sound pressure level of the residual noise to meet the predetermined target when the amplitude of the interference noise changes. [PERSON] et al. [23] proposed two improved LMS algorithms, namely iterative variable step size LMS (IVS-LMS) and variable tap length LMS (VT-LMS), for active in-vehicle noise control. Compared with the classical LMS, the IVS-LMS and VT-LMS algorithms can better improve the convergence speed and steady-state error of the algorithm. [PERSON] et al. [24] proposed an improved active sound quality control (ASQC) method for vehicle internal noise based on the adaptive least mean square (LMS) algorithm commonly used in active noise control (ANC), namely the post-masking LMS (PmLMS) algorithm, to improve the ride comfort of passengers. [PERSON] et al.