## 1 Introduction  
Active noise control has been present in research and development, as well as in practical applications in the last three decades [1; 2]. Personal noise control is one of the most successful implementations of the general principle, due to the small enclosure where noise reduction is to be achieved. Noise control in vehicles, airplanes and headphones are typical examples. Noise-canceling headphones have already become commercial products, and they are available at different prices and are popular among users [3]. Nowadays, offices are often noisy, especially so-called open offices. The main distractions are ringing phones, loud office machines, conversations, etc. Wearing a headphone equipped with active noise control is a straightforward solution to the problem. However, their long-time wear, up to a full eight-hour workday, is uncomfortable. An alternative solution to the noise control problem is an active headrest system built into a chair. Assuming that an office employee mostly sits in the chair, this is a suitable solution. To accommodate human ears, two secondary sources have to be located in the headrest. The installation details of the reference and error microphones and the specific control algorithm can be selected in different ways based on the objectives. Several similar noise-canceling systems are presented in the literature, e.g., [4; 5; 6; 7; 8]. They all discussed an active headrest system, and some of them introduced a noise-canceling chair specifically [6; 7]. The papers cited here show that there are some open algorithmic and technological questions. The papers report on experimental results, made in an anechoic or semi-anechoicchamber [7, 8] or in a laboratory [4; 5; 6]. In reflective rooms, the acoustic fields to be controlled are more complex, which limits the performance in a sense. A typical simplification is that the systems usually control periodic or complex tonal disturbances [5; 6; 7; 8]. The paper [4] includes experiments with broadband noise, as well. The systems described in [4; 5; 6; 7] utilized feedback control, and only [8] applied a feedforward control. The reason for that is two-fold. Firstly, feedback control requires simpler equipment. Secondly, in this noise control problem, the location of the primary sources is unknown in advance, so the reference microphones cannot be installed properly. However, feedback control is inappropriate or only slightly appropriate for broadband control. Narrowband and colored noise has been suppressed only by 4-7 dB by feedback control [4]. The main development has been focused on the acquisition of the error signals. Active control minimizes the acoustic pressure at the error microphones, resulting in a zone of quiet. Considering a diffuse sound field and a 10-dB reduction of the primary sound pressure level, this zone is a sphere-like volume with a diameter of about one tenth of the excitation wavelength [9]. Assuming noise control up to 1 kHz, the diameter of the zone is about 3.4 cm. This implies that error microphones built into the headrest cannot be sufficiently utilized. To overcome this problem, virtual sensing algorithms have been developed [10]. The aim of virtual sensing is to estimate both the primary and the secondary sound field at specified locations, away from the physically-installed microphones. The specified locations are the virtual locations, where the virtual or remote microphones are located. In the case of active headrest systems, physical microphones are usually built into the headrest, and virtual microphones are placed at the ears. As the ears' position can vary during the operation, head-tracking can also be applied. The paper [10] gives a comprehensive review of different algorithms; here, only the virtual microphone technique (VMT) and the remote microphone technique (RMT) are recalled. Both the VMT and the RMT estimate the secondary sound field by filtering the signals of the secondary sources.