# A Multi-Source Separation Approach Based on DOA Cue and DNN
## 1 Introduction
The TF mask is generally taken as the training target for a neural network, which represents the energy ratio of each sound source at a TF point. In a DNN-based separation method, an expectation maximization (EM) algorithm and a multichannel Wiener filter can also be combined to model the sound source spectrum and spatial information [18], which greatly improves the quality of the separated speech signal. In addition, some methods combining more than one DNN have been investigated in recent years. A DNN-based method combining a dereverberation module and a separation module for reverberant single-channel speaker separation was provided in [19]. [PERSON] proposed the two-stage network with an RNN and a DNN to separate multiple sound sources in reverberant environments [20]. Compared with previous methods based on a DNN, these proposed separation modules achieve better performance in reverberant environments. Nevertheless, sound source separation in reverberant environments is still a difficult problem which needs to be further investigated. In reverberant environments, the recorded signal contains the direct component of the sound source and the reverberant component from room and obstacle reflection. In the TF domain, it can be considered that a TF point is composed of a direct component and a reverberant component. The reverberant component changes the spectrum structure of the sound source signal and reduces the quality of the speech signal, which thus leads to the low quality of the separated source signals. A dereverberation module is introduced as a pre-processing step in the proposed method, which is proposed in [21]. Then, the dereverberation model aims to extract the clean mixed signal from the reverberant mixed signal recorded by sound field microphone. The multi-source separation framework for the dereverberated mixed signals is divided into two parts: sparse component point recovery and non-sparse component point recovery. In previous work, we proposed a multi-source localization algorithm, which can be used to provide necessary information for multi-source separation, such as sound source number and DOA estimates of sound sources [22]. We first determine the sparse and non-sparse component points by estimating the DOA of each TF point and that of real sound sources. The sparse component points are only dominated by the direct component of a sound source. In contrast, the non-sparse component points are composed of the components of multiple sound sources and/or the component of room reflection. Subsequently, we adopt different separation methods for sparse and non-sparse component points. Specifically, we first propose a sparse component point recovery method based on a DOA cue, where the DOA cue refers to the DOA estimates of sound sources. Subsequently, the TF mask of each non-sparse component point is estimated through regression training of the DNN, and the separated component from non-sparse component points can be further obtained. Finally, the post-processing methods including separated signal matching and smoothing are needed to obtain the separated signals from each sound source. The block diagram of the proposed multi-source separation algorithm is shown in Figure 1, where \"Format Transformation\" means that the A-format signal recorded by a sound field microphone is transformed into the B-format signal [23]. \"W-channel\" means the W-channel signal in B-format. In [24], we discussed a preliminary idea for multi-source separation. Different from [24], a DOA cue is used to realize sparse component point recovery; We focus on the effect of the dereverberation module on separation performance. In addition, a lot of experiments have been performed to verify the effectiveness of the proposed method. The remainder of the paper is organized as follows: The dereverberation model in the proposed multi-source separation method is introduced in Section 2. Section 3 presents the multiple-sound-source separation method.