# A Planer Moving Microphone Array for Sound Source Localization
## 1 Introduction
Similarly, to gain insight into the passive rotation of sound sources, in [23; 24; 25], the researchers focused on how acoustic factors can be estimated from sound sources rotating around signal receivers. Then, considering the rise in prominence of unmanned aerial systems (UASs), they drew conclusions based on noise generated from propellers rotating. In [26], [PERSON] et al. found that the UAS noise increases with pitch angles and the propellers' rotating velocity, but an irregular trend with vehicle speed was also shown. In addition to conducting research on the passive rotation of sound sources, researchers have also built active microphone arrays for acoustic research. In [27], [PERSON] et al. built a circular microphone array and rotated it to a certain degree to ensure optimized sound collection. In addition, they developed an interpolation algorithm so that they could collect virtual data, focusing on sound collected before and after rotation. In [24], [PERSON] et al. directly used a linear rotating microphone array mounted on an unmanned vehicle. In [25], [PERSON]. et al. mounted a binaural microphone on a service robot for SSL. The linear microphone array featured in this study is always rotating and collecting data for a machine learning network to predict where the speaker is. With moving [28; 29] and rotating microphone arrays [30; 31] collecting more data, the proposed system could not only form a linear array pattern but also form other special patterns to carry out 2D to 3D localization. In [32; 33; 34], indoor 3D sound source localization is accomplished by fixing microphones to walls and the corners of rooms while a four-planar moving microphone structure occupies a much smaller space. Providing height information could also enrich data or evidence for speech recognition. For example, a child and an adult would speak at different levels in a relatively dark room at nightfall. Height information would have better performance than pure vision judgement [35; 36]. Although the active microphone arrays mentioned above fulfilled their expectations, these arrays generally require a huge volume and lack the ability for height localization. That is to say, their SSL ability is 2D-based, not 3D-based. For this reason, this work proposes a four-planar moving microphone structure (FPMMS) to mimic a 16-microphone array, which has much smaller volume. A validation experiment was conducted in a normal classroom. For a fair comparison, we also built a 4 \(\times\) 4 microphone array, a circular 8-microphone array, and a 1 \(\times\) 4 microphone linear array as contrast groups. Since FPMMS can move in planar, the arrays in the contrast groups can all rotate around their centers to collect more data. This work describes results in terms of angular estimation accuracy, horizontal distance estimation accuracy, and vertical height estimation accuracy. The remainder of this paper is structured as follows: Section 2 introduces the FPMMS and describes the procedure carried out to build it. In Section 3, the FPMMS is compared with a linear rotating microphone array, a circular rotating microphone array, and a 16-microphone array. Finally, Section 4 presents the conclusions drawn from our research.