中圖分類號(hào)： TN912.3
文獻(xiàn)標(biāo)識(shí)碼： A
DOI：10.16157/j.issn.0258-7998.212404
中文引用格式： 張家揚(yáng)，童峰，陳東升，等. 面向多說(shuō)話人分離的深度學(xué)習(xí)麥克風(fēng)陣列語(yǔ)音增強(qiáng)[J].電子技術(shù)應(yīng)用，2022，48(5)：31-36.
英文引用格式： Zhang Jiayang，Tong Feng，Chen Dongsheng，et al. Deep learning microphone array speech enhancement for multiple speaker separation[J]. Application of Electronic Technique，2022，48(5)：31-36.

Deep learning microphone array speech enhancement for multiple speaker separation

Abstract： With the increase of human-computer voice interaction scenes in recent years, using microphone array speech enhancement to improve speech quality has become one of the research hotspots. Different from the ambient noise, the interfering speaker′s speech and the target speaker are the same speech signal in the multiple speaker separation scene, showing similar time-frequency characteristics, which poses a higher challenge to the traditional microphone array speech enhancement technology. For the multiple speaker separation scenario, the spatial response cost function of microphone array is constructed and optimized based on deep learning network. The desired spatial transmission characteristics of microphone array are designed through deep learning model training, so as to improve the separation effect by improving the beamforming performance. Simulation and experimental results show that this method effectively improves the performance of multiple speaker separation.

Key words : deep learning；microphone array；beamforming；LSTM