DNN-Based Speech Enhancement Using Soft Audible Noise Masking for Wind Noise Reduction  

作　　者：Haichuan Bai Fengpei Ge Yonghong Yan 

机构地区：[1]Key Laboratory of Speech Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences,Beijing 100190, China [2]University of Chinese Academy of Sciences, Beijing 100049, China [3]Xinjiang Laboratory of Minority Speech and Language Information Processing, Xinjiang Technical Institute of Physics and Chemistry,Chinese Academy of Sciences, Urumqi 830011, China

出　　处：《China Communications》2018年第9期235-243,共9页中国通信（英文版）

基　　金：partially supported by the National Natural Science Foundation of China (Nos.11590772, 11590770);the Pre-research Project for Equipment of General Information System (No.JZX2017-0994/Y306)

摘　　要：This paper presents a deep neural network(DNN)-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the psychoacoustic model is adopted to calculate the masking threshold from the estimated clean speech spectrum. The gain for noise suppression is obtained based on soft audible noise masking by comparing the estimated wind noise spectrum with the masking threshold. To deal with the abruptly time-varying noisy signals, two separate DNN models are utilized to estimate the spectra of clean speech and wind noise components. Experimental results on the subjective and objective quality tests show that the proposed algorithm achieves the better performance compared with the conventional DNN-based wind noise reduction method.This paper presents a deep neural network（DNN）-based speech enhancement algorithm based on the soft audible noise masking for the single-channel wind noise reduction. To reduce the low-frequency residual noise, the psychoacoustic model is adopted to calculate the masking threshold from the estimated clean speech spectrum. The gain for noise suppression is obtained based on soft audible noise masking by comparing the estimated wind noise spectrum with the masking threshold. To deal with the abruptly time-varying noisy signals, two separate DNN models are utilized to estimate the spectra of clean speech and wind noise components. Experimental results on the subjective and objective quality tests show that the proposed algorithm achieves the better performance compared with the conventional DNN-based wind noise reduction method.

关 键 词：wind noise reduction speech enhancement soft audible noise masking psychoacoustic model deep neural network 

分 类 号：TN912.35[电子电信—通信与信息系统]