Dear Editor, This letter proposes a new pattern matching method based on word embedding and dynamic time warping (DTW) to identify groups of similar alarm floods. First, messages are transformed into numeric values that represent alarms also reflect the relationships between occurrences. Then, similarities numerically encoded flood sequences calculated by DTW floods identified via clustering. T...

The nearest neighbor method together with the dynamic time warping (DTW) distance is one of the most popular approaches in time series classification. This method suffers from high storage and computation requirements for large training sets. As a solution to both drawbacks, this article extends learning vector quantization (LVQ) from Euclidean spaces to DTW spaces. The proposed LVQ scheme uses...

In the paper recently proposed Human Factor Cepstral Coefficients (HFCC) are used to automatic recognition of pathological phoneme pronunciation in speech of impaired children and efficiency of this approach is compared to application of the standard Mel-Frequency Cepstral Coefficients (MFCC) as a feature vector. Both dynamic time warping (DTW), working on whole words or embedded phoneme patter...

Abstract Monitoring rice planting areas and their phenological phases is crucial for yield estimation informed decision-making. This study proposed a unified method mapping field phenology with dynamic time wrapping (DTW) distance-based classifier its variant sub-DTW algorithm using Sentinel-1’s synthetic aperture radar (SAR) VH band. Field samplings were conducted broad landcover types in one ...

Access to labeled time series data is often limited in the real world, which constrains performance of deep learning models field analysis. Data augmentation an effective way solve problem small sample size and imbalance datasets. The two key factors are distance metric choice interpolation method. SMOTE does not perform well on because it uses a Euclidean interpolates directly object. Therefor...

Multivariate time series naturally exist in many fields, like energy, bioinformatics, signal processing, and finance. Most of these applications need to be able to compare these structured data. In this context, dynamic time warping (DTW) is probably the most common comparison measure. However, not much research effort has been put into improving it by learning. In this paper, we propose a nove...

Vector quantization (VQ) is a widely used matching algorithm for text-independent speaker recognition. In this paper we study the use of text-dependent speaker recognition in practical access control application. We compared dynamic time warping (DTW) to VQ-based matching using textdependent pass phrases. Our goal was to find out, how fixed phrase affects speaker recognition performance. We col...

This paper describes the experiments and observations for Query-by-Example Search on Speech Task (QUESST) at MediaEval 2014. In this paper, we describe two different representations of speech that were explored for the task. We also show the capabilities and limitations of non-segmental dynamic time warping (NS-DTW) technique for searching various types of queries. This paper mainly focuses on ...

With the widespread use of time-lapse data to understand cellular function, there is a need for tools which facilitate high-throughput analysis of data. We present a system for automated segmentation and cell cycle phase labelling based on aligning temporal signals of simple features directly to a reference signal using Dynamic Time Warping (DTW). This is shown to result in a very accurate temp...