# A Planer Moving Microphone Array for Sound Source Localization
## 1 Introduction  
With the recent advent of sound source localization (SSL) technology, it has been used in many different terminals, such as those pertaining to radars, headsets, service robots, and so on. In daily life, SSL could supplementarily assist audio conception for vision-driven applications [1]. However, when people talk about hearing ability, they are usually referring to direction recognition ability. Distance estimation is much less emphasized, since the human auditory system cannot estimate distance as accurately as direction [2, 3]. This is especially true for scenarios relating to indoor human and robot interaction at dawn, at dusk, and in the evening, when the lighting conditions are not sufficient for distance estimation, meaning direction estimation is severely degraded due to the sole reliance on visualization. SSL provides robots with an ability to hear from humans and know where the orders are coming from [1]. In the field of conventional SSL, many researchers have explored the relationships between various acoustic cues, discussing aspects such as spectral variance [2], inter-time difference (ITD), inter-level difference (ILD) [4], time difference of arrival (TDOA) [5], and the direct power-to-reverberant power ratio (DRR) [6]. For huge microphone arrays, beamforming-based methods have been developed. In beamforming-based methods, a beamformer is constructed by the steered response power with phase transform (SRP-PHAT) to sum the generalized cross-correlation phase transform (GCC-PHAT) [7]. SRP-PHAT improves the ability to remain robust against reverberant noise by utilizing thefault tolerance of multiple microphones. Considering the huge calculation load, SRP-PHAT has been optimized by numerous researchers [7; 8]. Subspace-based methods like multiple signal classification (MUSIC) represent other famous SSL methods. The MUSIC method decomposes the received signal covariance matrix into one called signal subspace and one called noise subspace and uses the orthogonality of the two spaces to find the peak of planar direction of the sound source [9; 10; 11; 12; 13; 14]. Similarly to SRP-PHAT, MUSIC requires a large amount of computation sources. At the same time, it requires a large-volume microphone array to ensure the SSL accuracy [15]. In addition to single-sound source localization, spatial spectrum processing [16; 17; 18] and time-frequency (TF) processing is effective when dealing with multi-target sound source localization. TF processing methods can localize the direction of multiple sound sources and count the total number of sound sources. These TF processing methods assume that, at most, one source is dominant over the others in the TF domain, factoring in the sparse property of speech signals. This assumption, also called the W-disjoint orthogonal (WDO) assumption [18], can simplify multiple-source localization on broadband to single-source localization in individual TF bins. However, when these algorithms are applied to a multiple-microphone array, around 16 microphones and a huge volume are required to ensure excellent performance. Special headsets have been designed for noise cancellation, as evidenced by [19; 20; 21], wherein researchers built a healthcare headset for children with autism spectrum disorder. These kinds of structures are hard to integrate practically in service robots. The researchers then derived solutions based on two perspectives: the first focused on building a smaller microphone array, while the second focused on building an active microphone array. The key idea behind making an active microphone array is to collect more data for processing. Before designing an active microphone array, first, researching human head rotation's impact on sound source collection is essential. In [22], [PERSON] et al. investigate the performance robustness of an active headset with virtual microphones against head rotation in a pure-tone-diffracted diffuse sound field.