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The recent success of deep neural networks (DNNs) in speech recognition can be attributed largely to their ability to extract a specific form of high-level features from raw acoustic data for subsequent sequence classification or recognition tasks. Among the many possible forms of DNN features, what forms are more useful than others and how effective these DNN features are in connection with the different...
Methods for hypothesis testing on zero-mean vector-valued signals often rely on a Gaussian assumption, where the second-order statistics of the observed sample are sufficient statistics of the conditional distribution. This yields fast and simple tests, but by using information-theoretic statistics one can relax the Gaussian assumption. We propose using Rényi's quadratic entropy as an alternative...
In this study we make use of Canonical Correlation Analysis (CCA) based feature selection for continuous depression recognition from speech. Besides its common use in multi-modal/multi-view feature extraction, CCA can be easily employed as a feature selector. We introduce several novel ways of CCA based filter (ranking) methods, showing their relations to previous work. We test the suitability of...
We propose a novel approach for detecting printed photos from natural scenes using a light-field camera. Our approach exploits the extra information captured by a light-field camera and the multiple views of scene in order to infer a compact feature vector from the variance in the distribution of the depth of the scene. We then use this feature for robust detection of printed photos. Our algorithm...
The neural network based features became an inseparable part of state-of-the-art LVCSR systems. In order to perform well, the network has to be trained on a large amount of in-domain data. With the increasing emphasis on fast development of ASR system on limited resources, there is an effort to alleviate the need of in-domain data. To evaluate the effectiveness of other resources, we have trained...
In this paper, we present a semi-automatic algorithm to detect faults in seismic datasets using Hough transform. As a multistage approach, our method first highlights the likely fault points from the discontinuity map of one seismic section. Hough transform is then applied to detect faults features. Considering geological constraints of faults, false features are removed using a double-threshold method...
Identification of bird species based on their vocalization is studied in this paper. The main focus is introducing a new parametric representation of bird sounds for automatic identification of their species. The method is based on the statistics of local temporal patterns in bird vocalization. Two different sets of bird species are used in the classification tests. The first set contains six species...
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