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 pro-posed, 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 per-forms well especially in high levels of additive noise.
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