# A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector
## 1 Introduction  
Perceived quality and intelligibility of speech signals are degraded by pervasive noise. This presents challenges to many applications, such as speech communication, hearing aids, and speech recognition. For these applications, speech enhancement is crucial to recover signals from the noisy speech. The enhancements offered by multichannel devices are usually greater than those of single-channel devices [1]. Recent studies indicate that it is beneficial to extract a desired speech signal by beamforming in noisy and reverberant environments, especially in high-level background noise [2; 3]. Traditional beamforming methods require a priori knowledge of the Direction of Arrival (DoA) or the transfer functions from an acoustic source to microphones [4]. It is a challenging task to estimate the spatial information of a microphone array in adverse acoustic conditions. According to the auditory masking effect, the time-frequency (T-F) masking technique applies a real-valued or binary mask on the signal's spectrum to filter out unwanted components, because the mask reserves speech-dominant T-F units and weakens noise-dominant T-F units [5]. Advanced beamforming operations require an estimate of the cross-power spectral density matrix of the noise. These statistics can be obtained by estimating spectral masks for speech and noise. Then, beamformers with a mask estimation network can also enhance the quality of speech. Networks are first applied in neural beamformers [6; 7] to estimate the time-frequency masks and then masks are applied on the signal's spectrum to predict speech and noise statistics. With these statistics, multichannel filter coefficients are computed basedon well-studied beamforming designs, such as Minimum Variance Distortion Response (MVDR) beamformers [8], Linearly Constrained Minimum Variance beamformers (LCMV) [9], and Generalized Eigenvalue (GEV) beamformers [10]. [PERSON] et al. [11] explored many popular data-dependent spatial filter design criteria and recognized several well-known beamforming criteria as special cases. [PERSON] et al. [12] proposed a Convolutional Neural Network (CNN)-based mask estimation, which was learned from all the channels simultaneously. The results have shown it is beneficial to utilize multi-channel information, while the approach is array-dependent. Many researchers choose to work on the single channel mask predictor because it can be applied to all kinds of array configurations [13]. Recent studies have mainly focused on how to design an efficient network structure for single channel mask prediction. The prediction can provide a more accurate mask that assigns a proportion of each T-F bin to each of the sources. A Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) cells [14] was used to estimate the time-domain spatial filter weights of a filter-and-sum beamformer for each channel separately, which are then convolved with the input signal to obtain the enhanced signal. Then, this work was extended to estimate frequency domain spatial filter weights in [15]. The recording quality of a microphone has an important impact on speech enhancement performance of the system. It is crucial to subtract the channel which captures the signal with less unwanted components as the reference microphone to provide data for mask prediction. [PERSON] et al. [16] proposed an attention mechanism for reference microphone selection, while [PERSON] [17] adopted the data cleaning and augment operations to attain data to yield better performance in following stages. However, these methods mainly focused on a learning-based approach to select desired data from existing channels, which made the system more complicated. Furthermore, there are no significant differences in the different channels of homogenous sensors. Hence, effective signals cannot be acquired. Given the trends above, in this paper, a novel method for speech enhancement that combines acoustic focus and Deep Neural Network (DNN)-based multi-channel beamforming is proposed.