scholarly journals Rapid annotation of interictal epileptiform discharges via template matching under Dynamic Time Warping

2016 ◽  
Vol 274 ◽  
pp. 179-190 ◽  
Author(s):  
J. Jing ◽  
J. Dauwels ◽  
T. Rakthanmanon ◽  
E. Keogh ◽  
S.S. Cash ◽  
...  
Keyword(s):  
Template Matching ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Epileptiform Discharges ◽  
Interictal Epileptiform Discharges ◽  
Dynamic Time

An Improved Method In Speech Signal Input Representation Based On DTW Technique For NN Speech Recognition System

2012 ◽  
Author(s):  
Rubita Sudirman ◽  
Sh. Hussain Salleh ◽  
Shaharuddin Salleh
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Conjugate Gradient ◽  
Template Matching ◽  
Dynamic Time Warping ◽  
Gradient Descent ◽  
Reference Frames ◽  
Time Warping ◽  
Dynamic Time ◽  
Quasi Newton

Kertas kerja ini membentangkan pemprosesan semula ciri pertuturan pemalar Pengekodan Ramalan Linear (LPC) bagi menyediakan template rujukan yang boleh diharapkan untuk set perkataan yang hendak dicam menggunakan rangkaian neural buatan. Kertas kerja ini juga mencadangkan penggunaan cirian kenyaringan yang ditakrifkan dari data pertuturan sebagai satu lagi ciri input. Algoritma Warping Masa Dinamik (DTW) menjadi asas kepada algoritma baru yang dibangunkan, ia dipanggil sebagai DTW padanan bingkai (DTW–FF). Algoritma ini direka bentuk untuk melakukan padanan bingkai bagi pemprosesan semula input LPC. Ia bertujuan untuk menyamakan bilangan bingkai input dalam set ujian dengan set rujukan. Pernormalan bingkaian ini adalah diperlukan oleh rangkaian neural yang direka untuk membanding data yang harus mempunyai kepanjangan yang sama, sedangkan perkataan yang sama dituturkan dengan kepanjangan yang berbeza–beza. Dengan melakukan padanan bingkai, bingkai input dan rujukan boleh diubahsuai supaya bilangan bingkaian sama seperti bingkaian rujukan. Satu lagi misi kertas kerja ini ialah mentakrif dan menggunakan cirian kenyaringan menggunakan algoritma penapis harmonik. Selepas kenyaringan ditakrif dan pemalar LPC dinormalkan kepada bilangan bingkaian dikehendaki, pengecaman pertuturan menggunakan rangkaian neural dilakukan. Keputusan yang baik diperoleh sehingga mencapai ketepatan setinggi 98% menggunakan kombinasi cirian DTW–FF dan cirian kenyaringan. Di akhir kertas kerja ini, perbandingan kadar convergence antara Conjugate gradient descent (CGD), Quasi–Newton, dan Steepest Gradient Descent (SGD) dilakukan untuk mendapatkan arah carian titik global yang optimal. Keputusan menunjukkan CGD memberikan nilai titik global yang paling optimal dibandingkan dengan Quasi–Newton dan SGD. Kata kunci: Warping masa dinamik, pernormalan masa, rangkaian neural, pengecaman pertuturan, conjugate gradient descent A pre–processing of linear predictive coefficient (LPC) features for preparation of reliable reference templates for the set of words to be recognized using the artificial neural network is presented in this paper. The paper also proposes the use of pitch feature derived from the recorded speech data as another input feature. The Dynamic Time Warping algorithm (DTW) is the back–bone of the newly developed algorithm called DTW fixing frame algorithm (DTW–FF) which is designed to perform template matching for the input preprocessing. The purpose of the new algorithm is to align the input frames in the test set to the template frames in the reference set. This frame normalization is required since NN is designed to compare data of the same length, however same speech varies in their length most of the time. By doing frame fixing, the input frames and the reference frames are adjusted to the same number of frames according to the reference frames. Another task of the study is to extract pitch features using the Harmonic Filter algorithm. After pitch extraction and linear predictive coefficient (LPC) features fixed to a desired number of frames, speech recognition using neural network can be performed and results showed a very promising solution. Result showed that as high as 98% recognition can be achieved using combination of two features mentioned above. At the end of the paper, a convergence comparison between conjugate gradient descent (CGD), Quasi–Newton, and steepest gradient descent (SGD) search direction is performed and results show that the CGD outperformed the Newton and SGD. Key words: Dynamic time warping, time normalization, neural network, speech recognition, conjugate gradient descent

scholarly journals IMPLEMETASI OFFLINE PENGENALAN SISTEM ISYARAT BAHASA INDONESIA MENGGUNAKAN METODE DYNAMIC TIME WARPING PADA PERANGKAT ANDROID

2015 ◽  
Vol 6 (2) ◽  
pp. 391
Author(s):  
Mohammad Iqbal ◽  
Endang Supriyati ◽  
Tri Listyorini
Keyword(s):  
Time Series ◽  
Template Matching ◽  
Dynamic Time Warping ◽  
Time Warping ◽  
Dynamic Time ◽  
Bahasa Indonesia ◽  
Data Template

ABSTRAK Algoritma Dynamic Time Warping (DTW) digunakan secara luas untuk berbagai penelitian, salah satunya di bidang bahasa isyarat. DTW adalah algoritma pencocokan pola (template matching) untuk mengukur kemiripan dua data sekuensial (time series) temporal yang berbeda waktu dan kecepatan. Pada penelitian ini disajikan implementasi algoritma DTW untuk pengenalan bahasa isyarat Indonesia (Sistem Isyarat Bahasa Indonesia SIBI) secara offline. Dataset yang digunakan dalam penelitian ini sebanyak 900 data untuk dengan jumlah kelas 50 kata isyarat, yaitu dengan rincian untuk masing-masing kelas adalah 3 data sebagai data template dan 15 data sebagai data testing. Hasil pengujian menunjukkan bahwa tingkat pengenalan atau nilai accuracy adalah 89,73%. Waktu rata-rata yang dibutuhkan adalah 654.59 milidetik untuk proses pengenalan satu data testing dengan menggunakan template sebanyak 3 data per kelas atau total template 150 data. Kata kunci: pengenalan, offline, SIBI, bahasa isyarat Indonesia, android.

scholarly journals Use of Dynamic Time Warping for Object Shape Classification through Signature

1970 ◽  
Vol 6 (1) ◽  
pp. 33-49 ◽  
Author(s):  
KC Santosh
Keyword(s):  
Template Matching ◽  
Dynamic Time Warping ◽  
Center Of Gravity ◽  
Shape Classification ◽  
Time Warping ◽  
Similarity Matching ◽  
Object Shape ◽  
Boundary Points ◽  
Engineering And Technology ◽  
Dynamic Time

This paper expresses an application of similarity matching of the signatures through DTW.Fundamental aspect of classification is template matching. The classification is robust tonoise, scaling, and rotation. Feature includes radius plus angle along the boundary points withrespect to center of gravity. The classification automatically and confidently discloses theshape of every object at once throughout page from top to bottom. The paper expresses itspromising results within an average of a few seconds (cheaper classification) for an object. Aseries of tests is done with all possible configurations of geometrical shapes.Keywords: Signature; Dynamic Time Warping; Uniform ScalingDOI: 10.3126/kuset.v6i1.3308Kathmandu University Journal of Science, Engineering and Technology Vol.6(1) 2010, pp33-49

A fast template matching method based on improved ring projection transformation and local dynamic time warping

Optik ◽  
2020 ◽  
Vol 216 ◽  
pp. 164954
Author(s):  
Yulin Wang ◽  
Wen Liu ◽  
Fei Li ◽  
Heng Li ◽  
Wenbin Zha ◽  
...  
Keyword(s):  
Template Matching ◽  
Dynamic Time Warping ◽  
Local Dynamic ◽  
Time Warping ◽  
Matching Method ◽  
Dynamic Time ◽  
Projection Transformation

scholarly journals IMU Consensus Exception Detection with Dynamic Time Warping—A Comparative Approach

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2237 ◽  
Author(s):  
Chan-Yun Yang ◽  
Pei-Yu Chen ◽  
Te-Jen Wen ◽  
Gene Eu Jan
Keyword(s):  
Template Matching ◽  
Dynamic Time Warping ◽  
Performance Comparison ◽  
Measurement Unit ◽  
Comparative Approach ◽  
Time Varying ◽  
Abnormality Detection ◽  
Time Warping ◽  
Motion Signal ◽  
Dynamic Time

A dynamic time warping (DTW) algorithm has been suggested for the purpose of devising a motion-sensitive microelectronic system for the realization of remote motion abnormality detection. In combination with an inertial measurement unit (IMU), the algorithm is potentially applicable for remotely monitoring patients who are at risk of certain exceptional motions. The fixed interval signal sampling mechanism has normally been adopted when devising motion detection systems; however, dynamically capturing the particular motion patterns from the IMU motion sensor can be difficult. To this end, the DTW algorithm, as a kind of nonlinear pattern-matching approach, is able to optimally align motion signal sequences tending towards time-varying or speed-varying expressions, which is especially suitable to capturing exceptional motions. Thus, this paper evaluated this kind of abnormality detection using the proposed DTW algorithm on the basis of its theoretical fundamentals to significantly enhance the viability of the methodology. To validate the methodological viability, an artificial neural network (ANN) framework was intentionally introduced for performance comparison. By incorporating two types of designated preprocessors, i.e., a DFT interpolation preprocessor and a convolutional preprocessor, to equalize the unequal lengths of the matching sequences, two kinds of ANN frameworks were enumerated to compare the potential applicability. The comparison eventually confirmed that the direct template-matching DTW is excellent in practical application for the detection of time-varying or speed-varying abnormality, and reliably captures the consensus exceptions.

Detection of EEG transients in neonates and older children using a system based on dynamic time-warping template matching and spatial dipole clustering

NeuroImage ◽  
2009 ◽  
Vol 48 (1) ◽  
pp. 50-62 ◽  
Author(s):  
Ardalan Aarabi ◽  
Kamran Kazemi ◽  
Reinhard Grebe ◽  
Hamid Abrishami Moghaddam ◽  
Fabrice Wallois
Keyword(s):  
Template Matching ◽  
Dynamic Time Warping ◽  
Older Children ◽  
Time Warping ◽  
Dynamic Time

scholarly journals Energy Disaggregation Using Elastic Matching Algorithms

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 71 ◽  
Author(s):  
Pascal A. Schirmer ◽  
Iosif Mporas ◽  
Michael Paraskevas
Keyword(s):  
Energy Consumption ◽  
Template Matching ◽  
Dynamic Time Warping ◽  
Minimum Variance ◽  
Time Warping ◽  
Matching Algorithm ◽  
Energy Disaggregation ◽  
Model Training ◽  
Dynamic Time ◽  
Incoming Energy

In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.

scholarly journals Detection of Freezing of Gait Using Template-Matching-Based Approaches

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Cheng Xu ◽  
Jie He ◽  
Xiaotong Zhang ◽  
Cunda Wang ◽  
Shihong Duan
Keyword(s):  
Template Matching ◽  
Dynamic Time Warping ◽  
High Efficiency ◽  
Freezing Of Gait ◽  
Experimental Tests ◽  
High Accuracy ◽  
Detection Methods ◽  
Time Warping ◽  
Experimental Accuracy ◽  
Dynamic Time

Every year, injuries associated with fall incidences cause lots of human suffering and assets loss for Parkinson’s disease (PD) patients. Thereinto, freezing of gait (FOG), which is one of the most common symptoms of PD, is quite responsible for most incidents. Although lots of researches have been done on characterized analysis and detection methods of FOG, large room for improvement still exists in the high accuracy and high efficiency examination of FOG. In view of the above requirements, this paper presents a template-matching-based improved subsequence Dynamic Time Warping (IsDTW) method, and experimental tests were carried out on typical open source datasets. Results show that, compared with traditional template-matching and statistical learning methods, proposed IsDTW not only embodies higher experimental accuracy (92%) but also has a significant runtime efficiency. By contrast, IsDTW is far more available in real-time practice applications.

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