摘要
随着近年来人机语音交互场景不断增加,利用麦克风阵列语音增强提高语音质量成为研究热点之一。与环境噪声不同,多说话人分离场景下干扰说话人语音与目标说话人同为语音信号,呈现类似的时、频特性,对传统麦克风阵列语音增强技术提出更高的挑战。针对多说话人分离场景,基于深度学习网络构建麦阵空间响应代价函数并进行优化,通过深度学习模型训练设计麦克风阵列期望空间传输特性,从而通过改善波束指向性能提高分离效果。仿真和实验结果表明,该方法有效提高了多说话人分离性能。
With the increase of human-computer voice interaction scenes in recent years, using microphone array speech enhance-ment 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.
作者
张家扬
童峰
陈东升
黄惠祥
Zhang Jiayang;Tong Feng;Chen Dongsheng;Huang Huixiang(Key Laboratory of Underwater Acoustic Communication and Marine Information Technology Ministry of Education,Xiamen University,Xiamen 361005,China;College of Ocean and Earth Sciences,Xiamen Univercity,Xiamen 361005,China;Shenzhen Research Institute of Xiamen Univercity,Shenzhen 518000,China)
出处
《电子技术应用》
2022年第5期31-36,共6页
Application of Electronic Technique
基金
国家自然科学基金项目(11274259)
深圳虚拟大学园扶持经费研发机构建设项目(YFJGJS1.0)。
