###### Abstract  
A hybrid active sound quality control system, in which a hybrid feedforward and feedback structure is applied, can not only be used in cases where the line-spectrum noise is obtained easily with reference sensors, but it can also improve the comfortability of noise and eliminate unexpected Gaussian random noise. However, the traditional structure for a hybrid active sound quality control system, whereby a reference signal in the feedback control structure is synthesized by the output signals of the feedforward control filter, feedback control filter, and line-spectrum noise cancellation control filter, introduces couplings of the three control filters. To remove the coupling interactions of the feedforward and feedback control structures and to reduce the complexity of the control system, two modified structures with less computational complexity or a smaller increase in computation are investigated in this paper. The first one involves a simplified structure in which the reference signal in the feedback control structure is replaced by the summation of the residual error signal and the output signal of the line-spectrum noise cancellation control filter, and the second one is a modified structure which integrates the output signals of the feedback control filter and the line-spectrum noise cancellation control filter for the reference signal in the feedback control structure. Numerical simulations are carried out to show the performance of the modified structures. The results illustrate that the two modified structures have the ability to cancel Gaussian random noise and to reduce or enhance the amplitude of line-spectrum noise to promote sound quality. Moreover, a simplified structure with a new leaky filtered-x least mean square (FxLMS) algorithm is proposed to upgrade the noise reduction performance and elevate stability in the feedback control structure. The effectiveness of the proposed algorithm also is proven by the simulation results. hybrid active sound quality control; line-spectrum noise; gaussian random noise +
Footnote †: journal: Applied  
To achieve the abovementioned goal, [PERSON] et al. put forward the adaptive active sound quality control algorithm to shape the residual noise [7]. It was developed on the basis of an adaptive noise equalizer (ANE) in which the pseudo-error noise was minimized instead of the residual noise to update the weight coefficient of the control filter [8; 9; 10]. Furthermore, there is a gain factor that one can attenuate, retain, and amplify noise to improve comfortability by setting up an appropriate gain factor value in the adaptive noise equalizer structure. One can combine the ANE structure with different adaptive control algorithms to achieve the control goal of sound quality [11]. For example, a post-masking least mean square (LMS) algorithm was presented to achieve effective sound quality control aimed at vehicle interior noise [12]. A broadband self-tuning ANE system utilizing the filtered-x least mean square (FxLMS) algorithm was researched in Reference [13]. A novel adaptive FxLMS control scheme with better performance of convergence was depicted to pursue sound quality control of sinusoidal noise [14]. It is noted that these control algorithms or control systems used only the feedforward control structure. The adaptive feedforward control structure is widely applied to ANC systems when a reference signal can be obtained effectively by using reference sensors [15]. However, if there is no suitable position to arrange the reference sensors, the causality and correlation requirement may not be satisfied such that the noise cannot be controlled validly by the feedforward structure [16]. Unlike the feedforward control system, the feedback ANC system does not need a reference sensor to acquire the primary noise information, and the reference signal is the summation of the error signal and the control filter output signal filtered by the estimated model of the secondary path [17; 18].