计算机工程与应用2019，Vol.55Issue(9)：38-42,5.DOI:10.3778/j.issn.1002-8331.1806-0449
特征联合优化深度信念网络的语音增强算法
Feature Joint Optimization of Deep Belief Network for Speech Enhancement
摘要
Abstract
Concerning the problem that the poor generalization ability of Deep Believe Network(DBN)which leads to poor speech enhancement performance, a regression DBN speech enhancement algorithm based on features jointing optimization is proposed. It is not necessary to make any assumptions about speech and noise in advance. The Log-Mel frequency Power Spectrum(LMPS)of speech is extracted to be used directly for constructing the enhanced speech signals to ensure the quality of speech hearing, and the Mel-Frequency Cepstral Coefficients(MFCC)of speech is extracted as an auxiliary features, respectively. All the parameters of the original deep belief network architecture are optimized by integrating the combination feature into DBN system. This joint optimization estimation scheme imposes MFCC constraints not available in the direct prediction of LMPS, and improves the generalization ability of the model to estimate the LMPS, and reconstructs the enhanced speech more accurately. Simulation results in different SNR enviroment show that compared with single feature optimization such as Log Power Spectrum(LPS)and LMPS, LMPS and MFCC joint optimization can enable the enhanced speech obtain higher PESQ and SNR, and improve speech quality and intelligibility.关键词
深度信念网络/语音增强/联合优化/回归Key words
Deep Believe Network(DBN)/ speech enhancement/ joint optimization/ regression分类
信息技术与安全科学引用本文复制引用
王雁,贾海蓉,吉慧芳,王卫梅..特征联合优化深度信念网络的语音增强算法[J].计算机工程与应用,2019,55(9):38-42,5.基金项目
国家自然科学基金(No.61371193) （No.61371193）
山西省自然科学基金(No.201701D121058). （No.201701D121058）
