2,280 research outputs found
A Subband-Based SVM Front-End for Robust ASR
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
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 -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 -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
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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