现提出一种基于BP神经网络的线性预测系数的求解方法。
In this paper, a method of the calculation of LPC coefficients is proposed by use of BP neural network.
LSP参数可以通过求解一个与线性预测系数(LPC)相关的非线性N阶方程得到。
LSP parameters can be obtained by solving an N-order non-linear equation relating to the linear predictive coding (LPC) coefficients.
计算结果表明，运用预测神经元方法所得到的线性预测系数，精度明显高于传统的杜宾算法和格型算法。
The calculation results indicate that the precision of method based on predictive neuron model is evidently higher than that of conventional Durbin algorithm and lattice algorithm.
本文应用全极点模型，提取语音信号的线性预测系数，并推导出其倒谱系数，获得线性预测倒谱差分，用以描述说话人声道的动态变化。
In this paper, we use full pole model to obtain speech signal LPC, then deduce it's LPCC, and we use the LPCC difference to describe speaker's track dynamic movement.
通过应用全极点模型，提取语音信号的线性预测系数，并推导出其倒谱系数，获得线性预测倒谱差分，用以描述说话人声道的动态变化。
By using full pole model, we obtained speech signal LPC, then deduced it's LPCC, and we used the LPCC difference to describe speaker's track dynamic movement.
采用线性规划方法确定最优组合的权系数，并对预测出的结果进行了分析和讨论。
The optimum-weight coefficients were obtained by the linear programming model, finally, the results were discussed.
该文建立了自然图像的小波包分解系数绝对值线性预测模型，提出了基于小波包变换的图像特征形成算法。
In this article, the linear magnitude predictor of wavelet packet decomposition coefficients, the creation algorithm of images characteristics based on wavelet packet transform have been introduced.
系统提取的音频信号特征为线性预测美尔倒谱系数（LPCMCC），采用动态时间规整（DTW）的识别算法。
The audio signal feature, in this scheme, is the LPC Mel Cepstrum Coefficient (LPCMCC) and recognition algorithm is Dynamic Time Warping (DTW).
利用神经网络所具有的输入-输出之间的高度非线性映射关系，给出一种利用BP神经网络模型预测木材径向导热系数的方法。
A method to predict the wood radial thermal conductivity based on back propagation (BP) neural network model which has non-linear relation highly was proposed.
利用神经网络所具有的输入-输出之间的高度非线性映射关系，给出一种利用BP神经网络模型预测木材径向导热系数的方法。
A method to predict the wood radial thermal conductivity based on back propagation (BP) neural network model which has non-linear relation highly was proposed.
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