###### Abstract
In addition, the area of the measured plane is limited to the dimension of the microphone array. Various acoustic beamformers are regarded as the most suitable approaches for sound source localization in the far field, such as speech enhancement and human-computer interactions in indoor public areas. Beamforming techniques have been developed for decades [20; 21]. In addition, an attempt to integrate neural networks (NNs) with acoustic beamformers has proved that NNs could work as front-end in ASR systems for practical SSL scenarios [22; 23; 24]. Every microphone channel signal of this neural mask-based beamformer is pre-processed through NNs directly, to achieve high recognition rates in far-field scenarios. With respect to acoustic beamformers, in the SSL area, most of the acoustic beamformers are operated in the frequency domain for more accurate localization performances and shorter processing durations, such as in the widely used, linearly constrained minimum variance (LCMV) method, the multiple signal classification (MUSIC) technique, and the well-known deconvolution methods, including the CLEAN algorithm and the deconvolution approach for the mapping of acoustic sources (DAMAS) algorithm [25; 26; 27; 28; 29; 30]. However, to simultaneously acquire the signal features of sound sources in the time domain with their spatial information, the use of the conventional delay-and-sum (DAS) beamforming algorithm is regarded as a prerequisite so that the localization, separation, and reconstruction approaches of the multiple sound sources can be achieved concurrently [20]. The time-domain DAS beamforming algorithm utilizes a weighting function to compensate for the time delay in each measurement channel, and the compensated signals reinforce each other so that their summation can be maximized [31]. [PERSON] et al. proved that a roughly estimated time series could be obtained at each scanning point with two sound sources in the scanning area, experimentally, by weighting different time delays and compensating for distance attenuations for each measurement channel [32]. Thus, after the location of the sound source is determined by calculating the maximum summation of the compensated signals on the scanning plane, a preliminary estimated signal of the dominant sound source can be obtained. Based on this, the main logic of the proposed signal processing approach is that, first, the location and rough time-domain features of the dominant sound source can be identified and characterized by a time-domain DAS beamformer. Then, the denoising approach is performed using supervised learning DNNs [33] so that the dominant sound source can be reconstructed. Subsequently, the signals from the dominant sound source are subtracted from the originally received signals, and the initial beamforming sound map is updated. Since the dominant sound source is localized and reconstructed, the received signals of all channels will be subtracted by this dominant source signal to perform the next iteration. By repeating this loop, multiple sound sources in the sound field can be localized and reconstructed. As the time consumption of time-domain DAS beamformer is extremely high, to achieve the real-time localization and separation performance, the computing speed could be improved by using a frequency-domain beamformer to estimate the initial location of the dominant sound source. Considering the broadband feature of the speech signal, a broadband weighted multiple signal classification (BW-MUSIC) method is used in this study to balance the selection of the computed frequency and computation time [34]. Moreover, to make full use of the feature information contained in the speech, four parameters are extracted from the pending signal: the mel-frequency cepstral coefficients (MFCC), amplitude modulation spectrum (AMS), gammatone filter bank power spectra (GFPS), and relative spectral transformed perceptual linear prediction coefficients (RASTA-PLP) [35,36].