Search - fast DTW

Search - fast DTW - List

[Speech/Voice recognition/combine] Great_Outdoors_by_sandals82 DL : 0: 一种简单有效的基于动态时变语音识别源码对于大多数研究者来说,寻找能够匹配二重时间序列信号的最佳途径是很重要的,因为它有许多重要的应用需求.DTW是实现这项工作的显著技术,尤其在语音识别技术领域,在这里一个测试信号被按照参照模板拉伸或压缩, -Searching for the best path that matches two time-series signals is the main task for many researchers, because of its importance in these applications. Dynamic Time-Warping (DTW) is one of the prominent techniques to accomplish this task, especially in speech recognition systems. DTW is a cost minimisation matching technique, in which a test signal is stretched or compressed according to a reference template. Although there are other advanced techniques in speech recognition such as the hidden Markov modelling (HMM) and artificial neural network (ANN) techniques, the DTW is widely used in the small-scale embedded-speech recognition systems such as those embedded in cell phones. The reason for this is owing to the simplicity of the hardware implementation of the DTW engine, which makes it suitable for many mobile devices. Additionally, the training procedure in DTW is very simple and fast, as compared with the HMM and ANN rivals.
Date : 2025-07-12 Size : 2.54mb User : 宋小小
[Speech/Voice recognition/combine] lbimproved_2009-12-29 DL : 0: 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
Date : 2025-07-12 Size : 14kb User : wangxuan
[Speech/Voice recognition/combine] LCS-VC60 DL : 0: 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
Date : 2025-07-12 Size : 151kb User : wangxuan
[Other] FastestDTW DL : 0: discription of the fast DTW algorithm
Date : 2025-07-12 Size : 555kb User : sawwqweq
[Algorithm] ndtw-master DL : 0: 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 * Manhattan, Euclidean, SquaredEuclidean distance measures * Optional boundary constraints (1, 1) and (m,n) * Sakoe-Chiba band * Ikatura paralelogram (custom slope constraint can be specified which results in such paralelogram) * C# code written with performance in mind (fast data structures, local variables caching for loops etc.)
Date : 2025-07-12 Size : 2.21mb User : graf