A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures

A novel approach to automatic speaker identification (ASI) is presented. Most of the present automatic speaker identification systems based on neural networks have no definite mechanisms to compensate for time distortions due to elocution. Such models have less precise information about the intraspeaker measure. The new combined approach uses both distortion-based and discriminant-based methods. The distortion-based and discriminant-based methods are dynamic time warping (DTW) and artificial neural network (ANN) respectively. This paper compares this new classifier with a pure neural net classifier for speaker identification. The performance of the combined classifier surpasses that of a pure ANN classifier for the conditions tested.

Download Full-text

Hierarchical clustering of time series data with parametric derivative dynamic time warping

Expert Systems with Applications ◽

10.1016/j.eswa.2016.06.012 ◽

2016 ◽

Vol 62 ◽

pp. 116-130 ◽

Cited By ~ 25

Author(s):

Maciej Łuczak

Keyword(s):

Time Series ◽

Hierarchical Clustering ◽

Dynamic Time Warping ◽

Time Series Data ◽

Series Data ◽

Time Warping ◽

Parametric Derivative ◽

Dynamic Time

Download Full-text

Motion Error Classification for Assisted Physical Therapy - A Novel Approach using Incremental Dynamic Time Warping and Normalised Hierarchical Skeleton Joint Data

Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods ◽

10.5220/0006108002810288 ◽

2017 ◽

Cited By ~ 4

Author(s):

Julia Richter ◽

Christian Wiede ◽

Bharat Shinde ◽

Gangolf Hirtz

Keyword(s):

Physical Therapy ◽

Dynamic Time Warping ◽

Time Warping ◽

Motion Error ◽

Novel Approach ◽

Error Classification ◽

Dynamic Time

Download Full-text

MULTIDIMENSIONAL SEQUENCE CLUSTERING WITH ADAPTIVE ITERATIVE DYNAMIC TIME WARPING

International Journal of Computing ◽

10.47839/ijc.18.1.1273 ◽

2019 ◽

pp. 53-59

Author(s):

Sergii Mashtalir ◽

Olena Mikhnova ◽

Mykhailo Stolbovyi

Keyword(s):

Dynamic Time Warping ◽

Sequence Matching ◽

Time Warping ◽

Self Organizing Maps ◽

Real Time Processing ◽

Huge Data ◽

Novel Approach ◽

Time Series Approach ◽

Financial Data Analysis ◽

Dynamic Time

Multimedia sequence matching is an urgent problem nowadays for the field of artificial intelligence. Despite great progress in this field of computer science, huge data arrays require near to real time processing, which significantly limits applicable methods. In this paper, the authors make an attempt of time series approach modification and enhancement with orientation for non-stationary data of different length. The processing procedure is enriched by sequences alignment with iterative dynamic time warping which implies matching two temporal data segments. Computational complexity is reduced due to Kohonen self-organizing maps applied for the purpose of clustering. Mathematical presentation is given in scalar, vector and matrix forms in order to cover all the possible use cases. An example of video sequence processing with the novel approach is provided to show its efficiency. The proposed technique can also be successfully implemented for natural language and signal processing, bioinformatics and financial data analysis.

Download Full-text