关键词: 低信噪比环境, 基音频率, 范数正则化的解相关集成学习神经网络模型（L2-DNNE）

Abstract: 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.

Key words: very low Signal Noise Ratio（SNR） environment, fundamental frequency, L2 Decorrelated Neural Network Ensembles（L2-DNNE）