摘要
传统基音检测方法中当信噪比较低时,会出现清浊音检测效果差、算法精度低、鲁棒性差的缺点。为了克服这些缺点,提出了一种基于两层神经网络的基音检测算法。该方法采用BP人工神经网络进行端点检测,再采用第二层BP神经网络进行清浊音分离,最后通过动态验证得到基音频率。实验结果证明,与传统的自相关法相比,该方法减少了倍频及半频的误差提取,提高了基音频率的提取精度。
Traditional pitch detection methods present poor voicing detection accuracy and robustness when the signal-to-noise ratio is low. To overcome these shortcomings, a two-layer neural network based pitch detection algorithm is pro-posed in this paper. The method utilizes the BP artificial neural network to conduct endpoint detection and then the second BP neural network to separate the clear sonant. At last, the dynamic test is adopted to get pitch frequency. The experimental results show that the proposed algorithm can decrease double and half frequency errors and improve the pitch detection accuracy compared with traditional autocorrelation function method.
出处
《计算机工程与应用》
CSCD
2014年第5期199-202,251,共5页
Computer Engineering and Applications
基金
国家自然科学基金(No.61073196)
关键词
基音频率
反向传播(BP)神经网络
自相关函数
平均幅度差函数
清浊音分离
pitch detection
Back Propagation（BP）neural network
autocorrelation function
average magnitude differ-ence function
separation of unvoiced sounds and voiced sounds
