Up to now, there have been many good detection algorithms to improve the efficiency of the pitch detection algorithm.
目前有许多较好的检测算法提高了基音检测算法的效率。
This paper discusses in detail the pitch detection algorithm of Mixed Excitation Linear Prediction (MELP) and an improved algorithm.
本文详细讨论了混合激励线性预测(MELP)的基音周期估计算法及其改进算法。
In the research of mixed excitation linear prediction speech coding, a new real-time normalized correlation function pitch detection algorithm is proposed.
在混合激励线性预测低速率语音编码研究中，提出了一种新的归一化自相关函数实时基音检测算法。
According to the mechanism of speech signal, an effective pitch detection algorithm by combined liner predictive coding with maximum likelihood is proposed.
根据语音信号产生机理，结合常用的线性预测和最大似然法，提出了一种有效的基音检测算法。
In speech signal processing, one of the key technologies is the pitch detection algorithm, and its efficiency directly affects the quality of speech's real-time processing.
自相关基音检测算法是语音信号处理的关键技术，算法的效率直接影响了语音信号实时处理的质量。
The autocorrelation pitch detection algorithm is the key technique for speech signal processing, and its efficiency directly affects the quality of real-time speech processing.
自相关基音检测算法是语音信号处理的关键技术，算法的效率直接影响语音信号实时处理的质量。
The traditional pitch detection algorithm based on wavelet transform extracts the fundamental frequency by comparing the positions of maximum wavelet coefficients of continuous scales.
传统的小波变换基频检测通过比较相邻尺度上的小波系数极值点来进行检测。
Compared with the conventional LPC algorithm, the m-lpc algorithm improves a lot in the aspects of excitation source, pitch detection and synthesis, etc.
与传统LPC的算法相比，M—LPC算法在激励源、基音检测及合成等方面都作了改进。
In the real time experimentation for pitch detection of audio signal, the algorithm ensures the optimal scale and extracts the maximum of wavelet transform, and it has achieved a better result.
在音频信号的实时基频检测实验中，该算法较准确地定位最佳尺度和极大值点，取得了较好的基频提取结果。
In order to provide an accurate-pitch-cycle speech for pith detection algorithm with varied noise and SNR, we use signal decomposition theory in pre-processing of pitch detection.
为了在不同噪声、信噪比下为基音检测算法提供更能准确反映基音周期实际变化的输入语音，本文将信号分解思想引入基音检测前端处理中。
In order to provide an accurate-pitch-cycle speech for pith detection algorithm with varied noise and SNR, we use signal decomposition theory in pre-processing of pitch detection.
为了在不同噪声、信噪比下为基音检测算法提供更能准确反映基音周期实际变化的输入语音，本文将信号分解思想引入基音检测前端处理中。
应用推荐