نتایج جستجو برای: dtw

تعداد نتایج: 1018  

2010
Atanas Ouzounov

In the study, the effectiveness of combinations of cepstral features, channel compensation techniques, and different local distances in the Dynamic Time Warping (DTW) algorithm is experimentally evaluated in the text-dependent speaker identification task. The training and the testing has been done with noisy telephone speech (short phrases in Bulgarian with length of about 2 seconds) selected f...

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پایان نامه :وزارت علوم، تحقیقات و فناوری - دانشگاه صنعتی شاهرود - دانشکده مهندسی برق 1391
هادی نادری, حسین مروی,

تشخیص کلمات کلیدی یا keyword-spotting در حالت کلی به معنای یافتن یک کلمهی کلیدی در یک پروندهی نوشتاری و یا گفتاری است. در این تحقیق، یک روش جدید تشخیص یا بازشناسی کلمات کلیدی در زبان فارسی، در دوحالت پیوسته و گسسته معرفی شده است. در هر دوحالت تشخیص کلمات کلیدی در گفتار پیوسته و گسسته، از روش dynamic time warping(dtw) استفاده شده است که با سیستمهایی که بر اساس مدل مخفی مارکوف طراحی شده و امروزه ...

2008
Luís Figueira Luís C. Oliveira

Currently, the majority of the text-to-speech synthesis systems that provide the most natural output are based on the selection and concatenation of variable size speech units chosen from an inventory of recordings. There are many different approaches to perform automatic speech segmentation. The most used are based on (Hidden Markov Models) HMM [1,2,3] or Artificial Neural Networks (ANN) [4], ...

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2010
Scott MacLean George Labahn

Dynamic time warping (DTW) is well known as an effective method for model-based symbol recognition. Unfortunately, its complexity is quadratic in the number of points present in the symbols to be matched. In this paper, we propose a greedy approximate solution to Tappert’s dynamic program formulation of DTW, and show empirically that it performs as well as the exact solution while requiring onl...

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Journal: :International Journal of Computer Applications 2015

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Journal: :Int. Arab J. Inf. Technol. 2014
Muhammad Tahir Tahir Mustafa Madani Sheikh Ziauddin Muhammad Arshad Awan Waqar Hussain Rana

This paper compares Dynamic Time Warping (DTW) and Waveform Matching (WFM), the two gesture recognition techniques, applied on a specific Squash game application we have developed. Our application gets accelerometer readings by moving the NintendoTM Wiimote in a 3D space. This application manipulates the Wiimote gestures in the form of signals. These signals are the effect of the movements, the...

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2016
Ahlam Hanoon Shini Zainab Ibrahim Abood Tariq Ziad Ismaeel N. Trivedi V. Kumar S. Kumar S. Ahuja R. Chadha S. B. R. C. Guido L. S. Vieira E. S. Fonseca F. L. Sanchez P. R. Scalassara C. D. Maciel J. C. Pereira S. H. Chen Z. I. Abood A. H. Al-sudani K R. Ghule R. R. Deshmukh K. Kannan S. A. Perumal K. Arulmozhi

Information processing has an important application which is speech recognition. In this paper, a two hybrid techniques have been presented. The first one is a 3-level hybrid of Stationary Wavelet Transform (S) and Discrete Wavelet Transform (W) and the second one is a 3-level hybrid of Discrete Wavelet Transform (W) and Multi-wavelet Transforms (M). To choose the best 3-level hybrid in each te...

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2006
Qiang HUO

We propose a dynamic time-warping (DTW) based distortion measure for measuring the dissimilarity between pairs of left-to-right continuous density hidden Markov models with state observation densities being mixture of Gaussians. The local distortion score required in DTW is defined as an approximate Kullback-Leibler divergence (KLD) between two Gaussian mixture models (GMMs). Several approximat...

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2011
Zied TRIFA Mohamed LABIDI Maher KHEMAKHEM

Distributed computing is the method of splitting a large problem into smaller pieces and allocating the workload among many computers. These individual computers process their portions of the problem, and the results are combined together to form a solution for the original problem. At present, Distributed computing systems can be broadly classified into two methods, namely Grid computing and V...

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1999
Eamonn J. Keogh Michael J. Pazzani

There has been much recent interest in adapting data mining algorithms to time series databases. Many of these algorithms need to compare time series. Typically some variation or extension of Euclidean distance is used. However, as we demonstrate in this paper, Euclidean distance can be an extremely brittle distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow ...

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function paginate(evt) { url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term pg=parseInt(evt.target.text) var data={ "year":filter_year, "term":term, "pgn":pg } filtered_res=post_and_fetch(data,url) window.scrollTo(0,0); } function update_search_meta(search_meta) { meta_place=document.getElementById("search_meta_data") term=search_meta.term active_pgn=search_meta.pgn num_res=search_meta.num_res num_pages=search_meta.num_pages year=search_meta.year meta_place.dataset.term=term meta_place.dataset.page=active_pgn meta_place.dataset.num_res=num_res meta_place.dataset.num_pages=num_pages meta_place.dataset.year=year document.getElementById("num_result_place").innerHTML=num_res if (year !== "unfilter"){ document.getElementById("year_filter_label").style="display:inline;" document.getElementById("year_filter_place").innerHTML=year }else { document.getElementById("year_filter_label").style="display:none;" document.getElementById("year_filter_place").innerHTML="" } } function update_pagination() { search_meta_place=document.getElementById('search_meta_data') num_pages=search_meta_place.dataset.num_pages; active_pgn=parseInt(search_meta_place.dataset.page); document.getElementById("pgn-ul").innerHTML=""; pgn_html=""; for (i = 1; i <= num_pages; i++){ if (i===active_pgn){ actv="active" }else {actv=""} pgn_li="
  • " +i+ "
    • "; pgn_html+=pgn_li; } document.getElementById("pgn-ul").innerHTML=pgn_html var pgn_links = document.querySelectorAll('.mypgn'); pgn_links.forEach(function(pgn_link) { pgn_link.addEventListener('click', paginate) }) } function post_and_fetch(data,url) { showLoading() xhr = new XMLHttpRequest(); xhr.open('POST', url, true); xhr.setRequestHeader('Content-Type', 'application/json; charset=UTF-8'); xhr.onreadystatechange = function() { if (xhr.readyState === 4 && xhr.status === 200) { var resp = xhr.responseText; resp_json=JSON.parse(resp) resp_place = document.getElementById("search_result_div") resp_place.innerHTML = resp_json['results'] search_meta = resp_json['meta'] update_search_meta(search_meta) update_pagination() hideLoading() } }; xhr.send(JSON.stringify(data)); } function unfilter() { url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":"unfilter", "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } function deactivate_all_bars(){ var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(bar) { bar.dataset.active = false bar.style = "stroke:#71a3c5;" }) } year_chart.on("created", function() { var yrchart = document.querySelectorAll('.ct-bar'); yrchart.forEach(function(check) { check.addEventListener('click', checkIndex); }) }); function checkIndex(event) { var yrchart = document.querySelectorAll('.ct-bar'); var year_bar = event.target if (year_bar.dataset.active == "true") { unfilter_res = unfilter() year_bar.dataset.active = false year_bar.style = "stroke:#1d2b3699;" } else { deactivate_all_bars() year_bar.dataset.active = true year_bar.style = "stroke:#e56f6f;" filter_year = chart_data['labels'][Array.from(yrchart).indexOf(year_bar)] url=/search_year_filter/ var term=document.getElementById("search_meta_data").dataset.term var data={ "year":filter_year, "term":term, "pgn":1 } filtered_res=post_and_fetch(data,url) } } function showLoading() { document.getElementById("loading").style.display = "block"; setTimeout(hideLoading, 10000); // 10 seconds } function hideLoading() { document.getElementById("loading").style.display = "none"; } -->