# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 5 Conclusions  
In this paper, aiming at the hearing technology of service robots, a novel target speech enhancement method has been proposed systematically with a single AVS to suppress spatial multiple interferences and additive background noise simultaneously. By exploiting the AVS signal model and its inter-sensor data ratio (ISDR) model, the desired monotone functions between ISDR and the DOA of the target speaker is derived. Accordingly, a nonlinear soft mask has been designed by making use of speech time-frequency (TF) sparsity with the known DOA of the target speaker. As a result, a single AVS-based speech enhancement method (named as AVS-SMASK) has been formulated and evaluated. Comparing with the existing AVS-FMV algorithm, extensive experimental results using simulated data and recorded data validate the effectiveness of our AVS-SMASK algorithm in suppressing spatial interferences and the additive background noise. It is encouraging to see that our AVS-SMASK algorithm is able to maintain less speech distortion. Due to page limitations, we did not show the derivation of the algorithm under reverberation. The signal model and ISDR model under reverberant conditions will be presented in our paper [27]. Our preliminary experimental results show that the PESQ of our proposed AVS-SMASK degrades gradually when the room reverberation becomes stronger (RT\(60>400\) ms), but LSD is not sensitive to the room reverberation. Besides, there is an argument that learning-based SE methods achieve the state-of-art. In our opinion, in terms of SNR, PESQ, and LSD, this is true. However, learning-based SE methods ask for large amounts of training data, and require much larger memory size and a high computational cost. In contrast, the application scenarios of this  
Figure 11: (Experiment 5) The performance versus the \(\phi_{s}\) mismatch \(\phi_{s}\)”. (**a**) PESQ results and (**b**) LSD results.  
research are different to learning-based SE methods, and our solution is more suitable for low-cost embedded systems. A real demo system was established in our lab, and the conducted trials further confirmed the effectiveness of our method where room reverberation is moderate (RT60 < 400 ms). We are confident that with only four-channel sensors and without any additional training data collected, the subjective and objective performance of our proposed AVS-SMASK is impressive. Our future study will investigate the deep learning-based SE method with a single AVS to improve its generalization and capability to handle different noise and interference conditions.  
Original draft preparation and writing, [PERSON] and Z.L.; Review & Editing, [PERSON], [PERSON] and [PERSON] carried out the studies of the DOA estimation and speech enhancement with Acoustic Vector Sensor (AVS), participated in algorithm development, carried out experiments as well as drafted the manuscript. [PERSON] contributed to the design of the experiments, analyzed the experimental results and helped to review and edit the manuscript. All authors read and approved the final manuscript.  
This research was funded by National Natural Science Foundation of China (No: 61271309), Shenzhen Key Lab for Intelligent MM and VR (ZDSYS201703031405467) and the Shenzhen Science & Technology Fundamental Research Program (JCYJ20170817160058246).  
The authors declare no conflict of interest.