# Sound Source Localization Indoors Based on Two-Level Reference Points Matching
## 1 Introduction  
Sound source localization (SSL) has received significant research attention in the field of audio signal processing, and it is widely used in intelligent robots, blind spot detection and underwater detection [1; 2; 3]. What is more, microphone array SSL is a spatial spectrum estimation problem for broadband short-time stationary signals, the research results of which can also be used in mobile communication, sonar detection and radar detection. Usually, traditional SSL methods can be divided into three categories: high-resolution spectral estimation method [4], steered beamforming method [5] and time delay of arrival (TDOA) method [6]. These methods can transform the spatial geometric relationship between the sound source and the microphone array into a spatial spectrum, spatial beam and TDOA, respectively, first and then work out the location of the sound source accordingly. Due to the low computational complexity and hardware cost, the TDOA SSL method is widely used in sound source location and tracking [7; 8]. As a parametric positioning method, the TDOA SSL method usually uses the space geometrical propagation model to obtain the position of sound source [9; 10; 11; 12; 13; 14]. In practice, the signal propagation model should be simplified as follows:  
1. The sound source is a particle without size and shape. 2. The signal propagates in a homogeneous space. 3. The sound signal is omnidirectional. The SSL methods based on a geometry model can achieve ideal results outdoors, where the actual signal propagation model is similar to the idealized simplification modelexplained above. However, due to the complexity of the indoor environment, the ideal signal propagation model may be altered by the multipath effect, shadowing effect, fading effect and delay distortion caused by walls, floors, furniture and ceilings [15; 16]. Meanwhile, it is difficult to provide compensation for model distortion analytically due to the high complexity of sound field characteristics indoors [17; 18]. As a non-parametric localization method, fingerprint-based localization can locate the target point by the matching between the real-time signal and the database that contains the historical location information of the service area. This method can take full advantage of the similarity of signal characteristics of adjacent samples in the service area and effectively reduce the location error caused by the modelling error and measurement error in the geometric model method indoors. Compared with the precise measurement requirements and the stringent restraint of the application scenario for the parameter positioning method, avoiding sharp changes in the positioning environment, as the only requirement for the fingerprint-based SSL method, it is much easier to be satisfied in practical applications [19; 20]. As the basis of fingerprint-based SSL indoors, the positioning database scale directly affects the positional accuracy of the SSL system [21; 22]. In practical applications, in order to make the location fingerprint database better reflect the distribution characteristics of the sound field, it is usually necessary to arrange a large number of sampling points in the location service area. However, the matching calculation for searching adjacent RPs from the large-scale database will greatly reduce the online positioning efficiency. Therefore, the fingerprint-based SSL encountered difficulties in applications with high real-time requirements such as mobile robot auditory positioning, indoor abnormal sound source positioning and speaker positioning [23]. In order to improve the efficiency of fingerprint-based SSL indoors, various methods are proposed to optimize the offline sampling process and the online positioning process. For the offline sampling phase, [PERSON] et al. [24] adopted the sparse database recovery method based on interpolation to reduce the initial RPs to improve the efficiency of offline sampling.