摘要
低信噪比环境下的基音频率检测极其重要且富有挑战性,至今未得到很好的解决。基于此,首先构造了基于PEFAC的频域空间检测模型,将基音频率作为特征进行提取,然后提出范数正则化的解相关集成学习神经网络模型(L2-DNNE)对其进行训练,利用负相关学习机制(NCL)和模型复杂度约束项提高集成学习模型的泛化能力,从而获取基音频率的最优值,且在测试精度和时间代价上取得了较好的平衡。将该算法与相关有代表性的算法进行比较。比较结果表明,该算法在不同类型不同程度的噪声环境下,能显著提升检测识别率,尤其在低信噪比下有更显著优势。
Fundamental frequency determination in low SNR noise environment is a challenging job,and has not been got solved well so far.Based on this,in this paper,firstly it builds a PEFAC based frequency-domain detection model,and then extracts the characteristic values of fundamental frequency.After that,a L2-DNNE based regression model is proposed,which can ensure the generation ability based on the NCL and model complexity adjustment,and beneficial to searching of the optimum,moreover the algorithm can obtain a balance on test accuracy and time cost.At last,it compares the performance of the algorithm with that of other representative algorithm.The experimental results show that it performs well especially in high levels of additive noise.
作者
张小恒
李勇明
朱斌
ZHANG Xiaoheng;LI Yongming;ZHU Bin(Chongqing Radio & TV University, Chongqing 400052, China;Research Institute of Signal and Information Processing, Chongqing University, Chongqing 400030, China)
出处
《计算机工程与应用》
CSCD
北大核心
2017年第11期155-160,共6页
Computer Engineering and Applications
基金
国家自然科学基金(No.61108086
No.91438104
No.61571069
No.81601970
No.61501065)
中国博士后科学基金(No.2013M532153)
中央高校基本科研业务费专项资金(No.CDJZR155507)
重庆市教委科学技术研究项目(No.KJ1603805)
重庆市基础科学与前沿技术研究专项(No.cstc2016jcyj A0043
No.cstc2016jcyj A0064
No.cstc2016jcyj A0134)
重庆市社会事业与民生保障科技创新专项(No.cstc2016shmszx40002)
重庆市博士后科研项目特别资助
教育部留学回国人员基金