# Adjustable Structure for Feedback Active Headest System Using the Virtual Microphone Method
###### Abstract  
Active headrest is an essential application for active noise control, capable of reducing low-frequency disturbance around an error microphone. However, in most cases, attaching microphones to ears is not feasible and noise attenuation performance is vulnerable to plant response variations. This paper presents a multichannel feedback active headrest system combined with the virtual microphone method and a manually adjustable headrest structure for users. Applying the virtual microphone method can transfer the attenuation target from the distant microphone to the ear. The proposed adjustable headrest structure allows for secondary loudspeakers and the corresponding microphone to be moved as a single unit while maintaining the relative distances between the secondary loudspeakers and microphones constant to ensure that the related plant responses are consistent. Experiments were conducted to validate the performance against multi-sinusoidal machine noise. The results demonstrate the benefits of the proposed structure over conventional structures. Additionally, the applicability of the three commonly used virtual sensing methods (the auxiliary filter method, remote microphone method, and virtual microphone method) in various practical conditions was verified when using the proposed adjustable headrest structure. Furthermore, 10 volunteers were involved in the evaluation, and the robustness of the proposed system for various users was confirmed. active noise control; active headrest; adjustable structure; multichannel adaptive feedback control; virtual microphone method; virtual sensing methods +
Footnote †: journal:  
0000-0001-9998-22000-2000-2000-2000-2000-2000-2000-2000-2000-2000-2000-2000-2000-2000-20000-2000  
called the internal model control (IMC) [4], significant levels of noise reduction can be achieved, especially for narrowband or periodic disturbances [9]. The filtered-reference normalized least mean square (FxNLMS) algorithm is a common adaptive algorithm used in ANC systems [10; 11] and aims to minimize the \(l_{2}\)-norm of the error signal vectors. Hence, the error microphone is required to be close enough to the target position because the ZoQ is generated around the microphone [12], even though it is inconvenient to place microphones on the ears. Therefore, virtual sensing (VS) methods have been proposed to solve this dilemma by transferring the ZoQ from the physical error microphones to the targets [13]. The ANC algorithms for the VS methods are divided into two stages. In the first stage, the microphones either are temporarily placed at the target positions to train additional filters that contain information concerning the optimal control filters, also known as the auxiliary filter method (AFM) [14; 15], or the observation filters are placed between the physical monitoring microphones and target microphones, also known as the remote microphone method (RMM) [16; 17; 18]. In the second stage, the temporary microphones are removed. The AFM estimates the disturbances at the targets implicitly from the reference signals with pre-trained additional filters. The RMM estimates the disturbances at the target locations explicitly from the monitoring signals with the pre-modeled observation filters [19]. The virtual microphone method (VMM) is a special form of the RMM [20]. In the case of the VMM, the disturbances at the physical monitoring microphone and the target microphone are assumed to be identical. Therefore, the VMM is slightly affected by variations in the primary source and is suitable for low-frequency signals with long wavelengths only [20]. In practice, head movement is common and may deteriorate the noise attenuation performance [6]. Although a head-tracking device can mitigate this effect [5; 17], additional estimation errors may become introduced, and head-mounted ANC systems are also impractical in most real-life applications [21; 22]. Moreover, the characteristics of the disturbance signal, the location, and quantity of primary noise may vary.