摘要
为提高语音增强算法消除方向性噪声和抑制混响的能力,结合单、多通道处理信号的优势,提出了双通道神经网络时频掩蔽语音增强算法.首先,利用改进的多分辨率耳蜗动静态特征,结合依据信噪比优化的自适应掩模,对双麦克风信号分别进行单通道神经网络初步语音增强,达到全面利用语音非线性特征改善感知度的目的;其次,提出一种基于自适应掩模方向矢量定位法,精确计算语音、噪声的空间协方差矩阵和方向矢量,在带噪和混响的环境下精确定位目标声源;最后,输入信号到卷积波束形成器中,进一步去噪和抑制混响.实验结果表明:与其他单、多通道语音增强算法相比,重构语音具有更好的语音质量和可懂度.
In order to improve the ability of speech enhancement algorithm eliminate directional noise and suppress reverberation,combining the advantages of single and multi-channel processing signals, a dual-channel neural network time-frequency masking speech enhancement algorithm was proposed. First, using the improved multi-resolution cochlear dynamic and static features(DSMRACC), combined with an adaptive mask(AM) optimized based on the signal-to-noise ratio(SNR), the dual-microphone signals were separately enhanced by a single channel deep neural network(DNN) to achieve the goal of fully utilizing the nonlinear features of speech to improve perception. Second, a steering vector localization method based on the AM was proposed to accurately calculate the spatial covariance matrix and steering vectors, locate the target speech accurately under the noise and reverberation environment.Finally,signal was input to a convolutional beamformer to further denoise and suppress reverberation.The experimental results show that compared with other speech enhancement algorithms, the enhanced speech has better speech quality and intelligibility.
作者
贾海蓉
梅淑琳
张敏
JIA Hairong;MEI Shulin;ZHANG Min(College of Information and Computer,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2021年第6期43-49,共7页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(12004275)
山西省留学人员科技活动择优资助项目(20200017)
山西省回国留学人员科研资助项目(2020042)。
关键词
语音增强
神经网络
动静态特征
自适应掩模
方向矢量
speech enhancement
neural network
dynamic and static characteristics
adaptive mask
steering vectors
