2,280 research outputs found

    A Subband-Based SVM Front-End for Robust ASR

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    This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-end that operates on an ensemble of the subband components of high-dimensional acoustic waveforms. The key issues of selecting the appropriate SVM kernels for classification in frequency subbands and the combination of individual subband classifiers using ensemble methods are addressed. The proposed front-end is compared with state-of-the-art ASR front-ends in terms of robustness to additive noise and linear filtering. Experiments performed on the TIMIT phoneme classification task demonstrate the benefits of the proposed subband based SVM front-end: it outperforms the standard cepstral front-end in the presence of noise and linear filtering for signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed front-end with a conventional front-end such as MFCC yields further improvements over the individual front ends across the full range of noise levels

    CKM Phenomenology and B-Meson Physics - Present Status and Current Issues

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    We review the status of the Cabibbo-Kobayashi-Maskawa (CKM) matrix elements and the CP-violating phases in the CKM-unitarity triangle. The emphasis in these lecture notes is on BB-meson physics, though we also review the current status and issues in the light quark sector of this matrix. Selected applications of theoretical methods in QCD used in the interpretation of data are given and some of the issues restricting theoretical precision on the CKM matrix elements discussed. The overall consistency of the CKM theory with the available data in flavour physics is impressive and we quantify this consistency. Current data also show some anomalies which, however, are not yet statistically significant. They are discussed briefly. Some benchmark measurements that remain to be done in experiments at the BB-factories and hadron colliders are listed. Together with the already achieved results, they will provide unprecedented tests of the CKM theory and by the same token may lead to the discovery of new physics.Comment: 66 pages, 13 figures, uses pr-imfp03-new.cls (enclosed); Lectures given at the International Meeting on Fundamental Physics, Soto de Cangas (Asturias), Spain, February 23 - 28, 2003 (to appear in the proceedings.

    DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion

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    Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately
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