Search results for: V. Kepuska

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Kepuska</title> <meta name="description" content="Search results for: V. Kepuska"> <meta name="keywords" content="V. Kepuska"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="V. 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Kepuska"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: V. Kepuska</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> A Simple Adaptive Atomic Decomposition Voice Activity Detector Implemented by Matching Pursuit</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Bryan">Thomas Bryan</a>, <a href="https://publications.waset.org/abstracts/search?q=Veton%20Kepuska"> Veton Kepuska</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivica%20Kostanic"> Ivica Kostanic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A simple adaptive voice activity detector (VAD) is implemented using Gabor and gammatone atomic decomposition of speech for high Gaussian noise environments. Matching pursuit is used for atomic decomposition, and is shown to achieve optimal speech detection capability at high data compression rates for low signal to noise ratios. The most active dictionary elements found by matching pursuit are used for the signal reconstruction so that the algorithm adapts to the individual speakers dominant time-frequency characteristics. Speech has a high peak to average ratio enabling matching pursuit greedy heuristic of highest inner products to isolate high energy speech components in high noise environments. Gabor and gammatone atoms are both investigated with identical logarithmically spaced center frequencies, and similar bandwidths. The algorithm performs equally well for both Gabor and gammatone atoms with no significant statistical differences. The algorithm achieves 70% accuracy at a 0 dB SNR, 90% accuracy at a 5 dB SNR and 98% accuracy at a 20dB SNR using 30dB SNR as a reference for voice activity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=atomic%20decomposition" title="atomic decomposition">atomic decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=gabor" title=" gabor"> gabor</a>, <a href="https://publications.waset.org/abstracts/search?q=gammatone" title=" gammatone"> gammatone</a>, <a href="https://publications.waset.org/abstracts/search?q=matching%20pursuit" title=" matching pursuit"> matching pursuit</a>, <a href="https://publications.waset.org/abstracts/search?q=voice%20activity%20detection" title=" voice activity detection"> voice activity detection</a> </p> <a href="https://publications.waset.org/abstracts/27613/a-simple-adaptive-atomic-decomposition-voice-activity-detector-implemented-by-matching-pursuit" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27613.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">290</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Atomic Decomposition Audio Data Compression and Denoising Using Sparse Dictionary Feature Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Bryan">T. Bryan </a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Kepuska"> V. Kepuska</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Kostnaic"> I. Kostnaic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A method of data compression and denoising is introduced that is based on atomic decomposition of audio data using “basis vectors” that are learned from the audio data itself. The basis vectors are shown to have higher data compression and better signal-to-noise enhancement than the Gabor and gammatone “seed atoms” that were used to generate them. The basis vectors are the input weights of a Sparse AutoEncoder (SAE) that is trained using “envelope samples” of windowed segments of the audio data. The envelope samples are extracted from the audio data by performing atomic decomposition with Gabor or gammatone seed atoms. This process identifies segments of audio data that are locally coherent with the seed atoms. Envelope samples are extracted by identifying locally coherent audio data segments with Gabor or gammatone seed atoms, found by matching pursuit. The envelope samples are formed by taking the kronecker products of the atomic envelopes with the locally coherent data segments. Oracle signal-to-noise ratio (SNR) verses data compression curves are generated for the seed atoms as well as the basis vectors learned from Gabor and gammatone seed atoms. SNR data compression curves are generated for speech signals as well as early American music recordings. The basis vectors are shown to have higher denoising capability for data compression rates ranging from 90% to 99.84% for speech as well as music. Envelope samples are displayed as images by folding the time series into column vectors. This display method is used to compare of the output of the SAE with the envelope samples that produced them. The basis vectors are also displayed as images. Sparsity is shown to play an important role in producing the highest denoising basis vectors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sparse%20dictionary%20learning" title="sparse dictionary learning">sparse dictionary learning</a>, <a href="https://publications.waset.org/abstracts/search?q=autoencoder" title=" autoencoder"> autoencoder</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20autoencoder" title=" sparse autoencoder"> sparse autoencoder</a>, <a href="https://publications.waset.org/abstracts/search?q=basis%20vectors" title=" basis vectors"> basis vectors</a>, <a href="https://publications.waset.org/abstracts/search?q=atomic%20decomposition" title=" atomic decomposition"> atomic decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=envelope%20sampling" title=" envelope sampling"> envelope sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=envelope%20samples" title=" envelope samples"> envelope samples</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabor" title=" Gabor"> Gabor</a>, <a href="https://publications.waset.org/abstracts/search?q=gammatone" title=" gammatone"> gammatone</a>, <a href="https://publications.waset.org/abstracts/search?q=matching%20pursuit" title=" matching pursuit"> matching pursuit</a> </p> <a href="https://publications.waset.org/abstracts/42586/atomic-decomposition-audio-data-compression-and-denoising-using-sparse-dictionary-feature-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42586.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">252</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Speaker Identification by Atomic Decomposition of Learned Features Using Computational Auditory Scene Analysis Principals in Noisy Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Bryan">Thomas Bryan</a>, <a href="https://publications.waset.org/abstracts/search?q=Veton%20Kepuska"> Veton Kepuska</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivica%20Kostanic"> Ivica Kostanic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time-frequency%20plane" title="time-frequency plane">time-frequency plane</a>, <a href="https://publications.waset.org/abstracts/search?q=atomic%20decomposition" title=" atomic decomposition"> atomic decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=envelope%20sampling" title=" envelope sampling"> envelope sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabor%20atoms" title=" Gabor atoms"> Gabor atoms</a>, <a href="https://publications.waset.org/abstracts/search?q=matching%20pursuit" title=" matching pursuit"> matching pursuit</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20dictionary%20learning" title=" sparse dictionary learning"> sparse dictionary learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20autoencoder" title=" sparse autoencoder"> sparse autoencoder</a> </p> <a href="https://publications.waset.org/abstracts/51069/speaker-identification-by-atomic-decomposition-of-learned-features-using-computational-auditory-scene-analysis-principals-in-noisy-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51069.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">289</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a 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Search results for: V. Kepuska