###### Abstract  
Synchronistical localization, separation, and reconstruction for multiple sound sources are usually necessary in various situations, such as in conference rooms, living rooms, and supermarkets. To improve the intelligibility of speech signals, the application of deep neural networks (DNNs) has achieved considerable success in the area of time-domain signal separation and reconstruction. In this paper, we propose a hybrid microphone array signal processing approach for the nearfield scenario that combines the beamforming technique and DNN. Using this method, the challenge of identifying both the sound source location and content can be overcome. Moreover, the use of a sequenced virtual sound field reconstruction process enables the proposed approach to be quite suitable for a sound field which contains a dominant, stronger sound source and masked, weaker sound sources. Using this strategy, all traceable, mainly sound, sources can be discovered by loops in a given sound field. The operational duration and accuracy of localization are further improved by substituting the broadband weighted multiple signal classification (BW-MUSIC) method for the conventional delay-and-sum (DAS) beamforming algorithm. The effectiveness of the proposed method for localizing and reconstructing speech signals was validated by simulations and experiments with promising results. The localization results were accurate, while the similarity and correlation between the reconstructed and original signals was high. b 2022 27 March 2022  
and estimating non-Gaussian sound sources without any prior knowledge of the mixing process [10; 13]. It is based on the optimization of a contrast function that measures the non-Gaussianity of the source. When the non-Gaussian nature of the modified signals is maximized, these modified signals are considered to be independent signals. As a result, the independent sources can be separated by iteratively modifying the mixed signals with a so-called unmixing matrix. The main advantage of the fast-ICA technique is its computational efficiency. These techniques have promising results for the separation of multiple independent sound sources. However, the locations of the sound sources cannot be determined directly after they are applied. The ASR system utilizes the artificial neural network (ANN) or DNN as a classification decoder to transcribe speech signals into words and text. Prior knowledge of speech signals, such as their acoustic features, and the pronunciation dictionary (PD) are required before processing [9]. Recently, the ASR system has been improved and developed to work with ego noise and to locate the sound source by binaural sound source localization (SSL) [14]. For the humanoid robot ASR system introduced in [14], the utilization of spatial information from binaural SSL could significantly improve the accuracy of speech recognition, even in a reverberant environment. As stated in [14], the spatial information decoded by microphones could be used to achieve multi-perception fusion between the auditory and visual senses of artificial intelligence (AI). However, except for the presence of prior knowledge, the other drawback of this method is that it requires a large number of sound sources. An alternative to the aforementioned methods is the larger microphone array, which could provide a more accurate coordinate value in the scanning area instead of an incident angle [15]. Using a large microphone array, the acoustic imaging and signal processing approach, which contains near-field acoustical holography (NAH) and beamforming, has been widely applied in various industrial scenarios [16; 17; 18; 19]. The NAH technique could map the sound field at every frequency of interest measured by the array. Therefore, a very precise sound source distribution can be provided because of the short-wave radiating distance between the microphone array plane and the sound source plane [18]. However, in many applications, a microphone array cannot be installed at such a short distance from the source plane.