• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.83-105
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• 2019

This study aims to suggest an effective method for the automatic classification of keywords with similar patterns by calculating pattern similarity of temporal data. For this, large scale news on the Web were collected and time series data composed of 120 time segments were built. To make training data set for the performance test of the proposed model, 440 representative keywords were manually classified according to 8 types of trend. This study introduces a Dynamic Time Warping(DTW) method which have been commonly used in the field of time series analytics, and proposes an application model, MA-DTW based on a Moving Average(MA) method which gives a good explanation on a tendency of trend curve. As a result of the automatic classification by a k-Nearest Neighbor(kNN) algorithm, Euclidean Distance(ED) and DTW showed 48.2% and 66.6% of maximum micro-averaged F1 score respectively, whereas the proposed model represented 74.3% of the best micro-averaged F1 score. In all respect of the comprehensive experiments, the suggested model outperformed the methods of ED and DTW.

• Journal of the military operations research society of Korea
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• v.17 no.1
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• pp.105-129
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• 1991

Dynamic Time Warping(in short DTW) is a kind of sequence comparison method. It is widely used in human speech recognition. The timing difference between two speech patterns to be compared is removed by warping the time axes of the speech pattern by minimising the time-normalised distance between them. In the process of finding the minimum time-normalised distance. the efficient method is dynamic programming problem. This paper describes the concept of dynamic time warping method, mathematical formulation and an application.

• The Journal of the Acoustical Society of Korea
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• v.19 no.8
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• pp.63-67
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• 2000

This paper deals with text-prompt speaker recognition based on dynamic time warping (DTW). The Genetic Algorithm was applied to the creation of reference patterns for suitable reflection of the speaker characteristics, one of the most important determinants in the fields of speaker recognition. In order to overcome the weakness of text-dependent and text-independent speaker recognition, the text-prompt type was suggested. Performed speaker identification and verification in close and open set respectively, hence the Genetic algorithm-based reference patterns had been proven to have better performance in both recognition rate and speed than that of conventional reference patterns.

• Phonetics and Speech Sciences
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• v.14 no.1
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• pp.29-36
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• 2022

The English word-final /rl/ cluster poses a particular problem for Korean learners of English because it is the sequence of two sounds, /r/ and /l/, which are not contrastive in Korean. This study compared the similarity distances between English and Korean speakers' /rl/ productions using the dynamic time warping (DTW) algorithm. The words with /rl/ (pearl, world) and without /rl/ (bird, word) were recorded by four English speakers and four Korean speakers, and compared pairwise. The F2-F1 trajectories, the acoustic correlate of velarized /l/, and F3 trajectories, the acoustic correlate of /r/, were examined. Formant analysis showed that English speakers lowered F2-F1 values toward the end of a word, unlike Korean speakers, suggesting the absence of /l/ in Korean speakers. In contrast, there was no significant difference in F3 values. Mixed-effects regression analyses of the DTW distances revealed that Korean speakers produced /r/ similarly to English speakers but failed to produce the velarized /l/ in /rl/ clusters.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.290-291
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• 2008

The most popular speech feature used in speech recognition today is the Mel-Frequency Cepstral Coefficients (MFCC) algorithm, which could reflect the perception characteristics of the human ear more accurately than other parameters. This paper adopts MFCC and its first order difference, which could reflect the dynamic character of speech signal, as synthetical parametric representation. Furthermore, we quote Dynamic Time Warping (DTW) algorithm to search match paths in the pattern recognition process. We use the software "GoldWave" to record English digitals in the lab environments and the simulation results indicate the algorithm has higher recognition accuracy than others using LPCC, etc. as character parameters in the experiment for Digital Isolated Word Recognition (DIWR) system.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.559-566
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• 2008

In this paper, we propose a range subsequence matching under dynamic time warping (DTW) distance. We exploit Dual Match, which divides data sequences into disjoint windows and the query sequence into sliding windows. However, Dual Match is known to work under Euclidean distance. We argue that Euclidean distance is a fragile distance, and thus, DTW should be supported by Dual Match. For this purpose, we derive a new important theorem showing the correctness of our approach and provide a detailed algorithm using the theorem. Extensive experimental results show that our range subsequence matching performs much better than the sequential scan algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1143-1149
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• 2010

In this study, we propose a novel method to detect and enhance the feature of ST-segment which offers the crucial information for the diagnosis of myocardial infarction and ischemia. With this aim, PQRST features of Electrocardiogram initially are detected and subsequently ST-segment are estimated. And Dynamic Time Warping(DTW) transformation is applied recursively to minimize the difference in time between ST-segments and calculate the minimum cumulative distance that decides the degree of similarity among ST-segments. As of the results, the inherent characteristic of ST-segment can be emphasized in terms of time parameter and thus the diagnostic features of a ST-segment can be revealed further.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.2
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• pp.21-28
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• 2010

This paper reports an experimental result on optimizing dissimilarity-based classification(DBC) by simultaneously using a dynamic time warping(DTW) and a multiple fusion strategy(MFS). DBC is a way of defining classifiers among classes; they are not based on the feature measurements of individual samples, but rather on a suitable dissimilarity measure among the samples. In DTW, the dissimilarity is measured in two steps: first, we adjust the object samples by finding the best warping path with a correlation coefficient-based DTW technique. We then compute the dissimilarity distance between the adjusted objects with conventional measures. In MFS, fusion strategies are repeatedly used in generating dissimilarity matrices as well as in designing classifiers: we first combine the dissimilarity matrices obtained with the DTW technique to a new matrix. After training some base classifiers in the new matrix, we again combine the results of the base classifiers. Our experimental results for well-known benchmark databases demonstrate that the proposed mechanism achieves further improved results in terms of classification accuracy compared with the previous approaches. From this consideration, the method could also be applied to other high-dimensional tasks, such as multimedia information retrieval.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.40-43
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• 1999

본 연구에서는 고립단어 인식시스템에 사용되고 있는 DTW(DynamicTimeWarping) 알고리즘의 계산량을 줄일 수 있는 방법을 제안한다. 일반적으로 고립단어 인식시 가장 인식률이 좋은 알고리즘은 DW라고 알려져 있으나, 인식대상어휘가 늘어나면 계산량이 비례해서 늘어나고 인식률이 저하되는 단점이 있으므로 일반적으로 200단어 이하의 어휘에만 사용되고 있다. 따라서 대상어휘를 감소시켜 계산량을 줄이기 위해 본 논문에서는 유성/무성/묵음 (V/U/S) 정보를 이용하여 코드워드를 구성하고 같은 코드워드에 해당되는 단어들을 추출해이들 만을 비교대상 어휘로 제한하므로서 DW 알고리즘을 적용할 대상 어휘수를 줄이는 방법을 사용하여 계산 속도를 향상시켰다 또한 입력 단어와 대상 단어와의 누적거리 계산 시 끝점 정보 뿐 만 아니라 유성/무성/묵음 경계 정보를 이용하여 piecewise DTW를 구현함으로서 탐색 영역을 축소함으로써 추가적인 계산량 감소가 가능하다. 따라서 상기 기법들을 이용하면 PC상에서도 DTW를 이용한 대어휘 고립단어 음성 인식기의 구현이 가능할 것이다.

• Information Systems Review
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• v.26 no.2
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• pp.83-93
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• 2024

This study proposes a preprocessing methodology based on Dynamic Time Warping (DTW) and k-Nearest Neighbors (kNN) to effectively represent time series data for predicting the completion quality of electroplating baths. The proposed DTW-based kNN preprocessing approach was applied to various regression models and compared. The results demonstrated a performance improvement of up to 43% in maximum RMSE and 24% in MAE compared to traditional decision tree models. Notably, when integrated with neural network-based regression models, the performance improvements were pronounced. The combined structure of the proposed preprocessing method and regression models appears suitable for situations with long time series data and limited data samples, reducing the risk of overfitting and enabling reasonable predictions even with scarce data. However, as the number of data samples increases, the computational load of the DTW and kNN algorithms also increases, indicating a need for future research to improve computational efficiency.