Segmental dynamic time warping (DTW) has been demonstrated to be a useful technique for finding acoustic similarity scores between segments of two speech utterances. Due to its high computational requirements, it had to be computed in an offline manner, limiting the applications of the technique. In this paper, we present results of parallelization of this task by distributing the workload in e...

Extreme Learning Machine (ELM) represents a popular paradigm for training feedforward neural networks due to its fast learning time. This paper applies the technique for the automatic classification of speech utterances. Power Normalized Cepstral Coefficients (PNCC) are employed as feature vectors and ELM performs the final classification. Both the baseline ELM algorithm and ELM with kernel hav...

Dynamic Time Warping (DTW) is considered as a robust measure to compare numerical time series when some time elasticity is required. Even though its initial formulation can be slow, extensive research has been conducted to speed up the calculations. However, those optimizations are not always available for multidimensional time series. In this paper, we focus on time series describing gesture m...

In this paper, we present a pattern recognition method that uses dynamic programming (DP) for the alignment of Radon features. The key characteristic of the method is to use dynamic time warping (DTW) to match corresponding pairs of the Radon features for all possible projections. Thanks to DTW, we avoid compressing the feature matrix into a single vector which would otherwise miss information....

<p><span>Dynamic time warping (DTW) is an important metric for measuring similarity most series applications. The computations of DTW cost too much especially with the gigantic sequence databases and lead to urgent need accelerating these computations. However, multi-core cluster systems, which are available now, their scalability performance/cost ratio, meet more powerful efficient...

Many time series data mining problems can be solved with repeated use of distance measure. Examples such tasks include similarity search, clustering, classification, anomaly detection and segmentation. For over two decades it has been known that the Dynamic Time Warping (DTW) measure is best to for most tasks, in domains. Because classic DTW algorithm quadratic complexity, many ideas have intro...

This paper presents an accelerometer-based pen device for online handwriting recognition applications. The accelerometer-based pen device consists of a triaxial accelerometer, a microcontroller, and an RF wireless transmission module. Users can hold the pen device to write numerals in air without space limitations. The accelerations generated by hand motions are generated by the accelerometer e...

The success of deep learning in the field state-of-health (SOH) estimation relies on a large amount battery data and fact that all possess same probability distribution. While real situations, model based one working condition set may not be valid for another due to distribution differences. Therefore, this article proposes transfer method using soft-dynamic time warping (soft-DTW) as statistic...

The aim of this paper is to characterise the green bond market in Visegrad Group countries (V4) and identify determinants benefits issuing bonds. specific objective a spatial–temporal analysis yield V4 countries. following research methods were used paper: source literature report analysis, statistical data (from international financial markets), Dynamic Time Warping method (DTW). DTW comprises...

Remaining useful life prediction based on trajectory similarity is a typical example of instance-based learning. Hence, Euclidean distance has the problems matching and low accuracy. Therefore, an engine remaining (RUL) method dynamic time warping (DTW) proposed. First, aiming at problem structure complexity multiple monitoring parameters, principal component analysis used to reduce dimension m...