当前位置：
首页 资源下载
搜索资源 - dynamic time warping C
搜索资源列表
-
0下载：
DTW（Dynamic Time Warping）算法，c代码
使用：
dtw infile1 infile2 outfile xsize ysize params [debug_file]
-
-
0下载：
C语言DTW算法实现,主要功能:
1)快速近邻DTW比较
2)算法稳定
3)节省内存-== Key features ==
1) Fast Dynamic Time Warping nearest neighbor cost retrieval.
2) Persistence
3) External-memory: you need only a constant amount of RAM
-
-
1下载：
本 文 首先 介绍了语音识别的研究和发展状况，然后循着语音识别系统的
处理过程，介绍了语音识别的各个步骤，并对每个步骤可用的几种方法在实
验基础上进行了分析对比。研究了语音信号的预处理和特征参数提取，包括
语音信号的数字化、分帧加窗、预加重滤波、端点检测及时域特征向量和变
换域特征向量.其中端点检测采用双门限法.通过实验比对特征参数的选取，
采用12阶线性预测倒谱系数作为识别参数。详细分析了特定人孤立词识别算
法，选定动态时间弯折为识别算法，并重点介绍其设计实现。
在
-
-
0下载：
Dynamic time warping program. Calculates the similarity between 2 vectors. Audio, Video, Speech Processing. DTW Dynamic Time Warping-DTW
-
-
0下载：
DTW stands for Dynamic Time Warping. It is the algorithm about how computer understands human voices. You can download the DTW Program written by Woohyung Chun
-
-
0下载：
dynamic time warping is a method that using for matching in speaker identification. this code i write in C# using Microsoft visual studio 2008
-
-
0下载：
Dynamic Time Warping algorithm, implemented in C#
-
-
0下载：
动态时间规整算法的C程序，源码，仅供参考~-Dynamic time warping algorithm C program source code, for reference ~
-
-
0下载：
动态时间弯曲带有路径约束的算法，用于时间序列度量时判别最有约束的c代码-Dynamic time warping algorithm with a path constraint, when used to determine the most time-series measure binding of the c code
-
-
0下载：
基于动态时间规整的语音识别系统研究及其实现，用C语言编程完成-Dynamic time warping-based speech recognition system and its implementation, complete with C language programming
-
-
0下载：
C语言DTW算法实现,主要功能:
1)快速近邻DTW比较
2)算法稳定
3)节省内存-== Key features ==
1) Fast Dynamic Time Warping nearest neighbor cost retrieval.
2) Persistence
3) External-memory: you need only a constant amount of RAM
-
-
1下载：
语音识别中的DTW（Dynamic Time Warping，动态时间归整）算法的c语言实现。-Voice recognition of the DTW (Dynamic Time Warping, dynamic time of the whole) algorithm c language.
-
-
0下载：
NDtw
====
Dynamic Time Warping (DTW) algorithm implementation for .NET C#
Features
====
* Single or multivariate
* Data preprocessing options (none, centering, normalization, standardization)
* Optional weights for variables
-
-
0下载：
Copyright (C) 2013 Quan Wang <wangq10@rpi.edu>,
* Signal Analysis and Machine Perception Laboratory,
* Department of Electrical, Computer, and Systems Engineering,
* Rensselaer Polytechnic Institute, Troy, NY 12180, USA
*/
/**
-
