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Alzheimer's disease (AD) is one major cause of dementia. Previous studies have indicated that the use of features derived from Positron Emission Tomography (PET) scans lead to more accurate and earlier diagnosis of AD, compared to the traditional approach used for determining dementia ratings, which uses a combination of clinical assessments such as memory tests. In this study, we compare Naïve Bayes...
In this paper, we explore the use of naive Bayes classifiers for music classification and retrieval. The motivation is to employ all audio features extracted from local windows for classification instead of just using a single song-level feature vector produced by compressing the local features. Two variants of naive Bayes classifiers are studied based on the extensions of standard nearest neighbor...
This paper addresses the problem of classifying altimetric signals according to their shapes. The proposed classifier is divided into three steps. A one-class support vector machine method is first used to isolate the large amount of Brown-like echoes from others signals which are considered as outliers. The second step extracts pertinent features from the the remaining echoes (which cannot be well...
In recent years Microelectrode recording (MER) analysis has proved to be a powerful localization tool of basal ganglia for Parkinson disease's treatment, especially the Subthalamic Nucleus (STN). In this paper, a signal-dependent method is presented for identification of the STN and other brain zones in Parkinsonian patients. The proposed method, refereed as optimal wavelet feature extraction method...
In this paper, a new classification method based on relevance vector machine (RVM) is used in the MPSK signals classification. Compared with the support vector machine (SVM), RVM is sparse model in the Bayesian framework, not only the solution is highly sparse, but also it does not need to adjust model parameter and its kernel functions don't need to satisfy Mercer's condition. The fourth order cumulants...
The problem of probability density estimation can be used in many areas in signal processing, such as regression and classification. In this paper, a density estimation approach based on support vector machine (SVM) was developed. Our algorithm has robust results and sparse solutions compared with Parzen's method. Besides, we used fundamental splines instead of Gaussian kernels in order to further...
This paper presents the results of the application of a feature selection procedure to an automatic music genre classification system. The classification system is based on the use of multiple feature vectors and an ensemble approach, according to time and space decomposition strategies. Feature vectors are extracted from music segments from the beginning, middle and end of the original music signal...
In this paper, we investigate two important issues that influence dialect classification: (i) exploring dialect dependent features, and (ii) an effective way of combining spectral, excitation, and vocal tract information to improve dialect classification. The motivation is that dialect dependent features such as formants, LSP (line spectral pairs) and MEPZ (MFCCs + energy + pitch) span a wider range...
We introduce a novel strategy for speaker verification based on the conception of a classifier which is independent of the target speaker, as opposed to traditional systems where the classifier is always target dependent. The basic principle is to build a system that decides whether two sequences were pronounced by the same speaker. In our view, this system is aimed to complement traditional ones...
In this study we have investigated the classification of old myocardial infarction through the analysis of 192 lead body surface potential maps (BSPM). Following an analysis of the most prominent features based on a signal to noise ratio ranking criterion the top 6 features were selected. These features were subsequently used as inputs to a series of supervised classification models in the form of...
Brain-computer interface (BCI) is based on processing signals recorded from the scalp, the surface of the cortex or from the inside of the brain in order to identify desired actions or behaviors. In BCI we are interested in extracting the most effective features from rare data in order to have the desired classification results. In this paper besides proposing two discrimination algorithms for classifying...
In this work we strive to find an optimal set of acoustic features for the discrimination of speech, monophonic singing, and polyphonic music to robustly segment acoustic media streams for annotation and interaction purposes. Furthermore we introduce ensemble-based classification approaches within this task. From a basis of 276 attributes we select the most efficient set by SVM-SFFS. Additionally...
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