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Patient monitoring is an important part of the overall treatment plan for hospital in-patients. However, monitoring is often time consuming for hospital staff. Staff must either remain in a patient's room, check in on the patient with frequent intervals or remotely monitor the patient via video surveillance. Constant monitoring may be disruptive to the patient as he or she attempts to rest. Furthermore,...
Many modern face verification algorithms use a small set of reference templates to save memory and computational resources. However, both the reference templates and the combination of the corresponding matching scores are heuristically chosen. In this paper, we propose a well-principled approach, named sparse support faces, that can outperform state-of-the-art methods both in terms of recognition...
Speech recognition systems generally use delta and delta-delta (velocity and acceleration) coefficients to characterise the dynamics apparent in frame-based representations of speech. These coefficients can be thought of as the errors of simple predictors. This paper describes the use of error coefficients derived from more advanced (and accurate) forms of prediction and interpolation. Both overall...
Speaker identification (SID) in cochannel speech, where two speakers are talking simultaneously over a single recording channel, is a challenging problem. Previous studies address this problem in the anechoic environment under the Gaussian mixture model (GMM) framework. On the other hand, cochannel SID in reverberant conditions has not been addressed. This paper studies cochannel SID in both anechoic...
A group of junior and senior researchers gathered as a part of the 2014 Frederick Jelinek Memorial Workshop in Prague to address the problem of predicting the accuracy of a nonlinear Deep Neural Network probability estimator for unknown data in a different application domain from the domain in which the estimator was trained. The paper describes the problem and summarizes approaches that were taken...
This paper presents a novel image classification method based on integration of EEG and visual features. In the proposed method, we obtain classification results by separately using EEG and visual features. Furthermore, we merge the above classification results based on a kernelized version of Supervised learning from multiple experts and obtain the final classification result. In order to generate...
This paper presents pattern classification to a predefined set of classes as a missing data task. This is achieved by first augmenting the feature vector of each training pattern with the corresponding binary codeword representing its class. A Restricted Boltzmann Machine (RBM) or a Dictionary Learning (DL) algorithm is then trained on the augmented feature space. During the classification stage,...
In regression analysis, outliers in the data can induce a bias in the learned function, resulting in larger errors. In this paper we derive an empirically estimable bound on the regression error based on a Euclidean minimum spanning tree generated from the data. Using this bound as motivation, we propose an iterative approach to remove data with noisy responses from the training set. We evaluate the...
Recurrent neural networks (RNNs) have recently been applied as the classifiers for sequential labeling problems. In this paper, deep bidirectional RNNs (DBRNNs) are applied for the first time to error detection in automatic speech recognition (ASR), which is a sequential labeling problem. We investigate three types of ASR error detection tasks, i.e. confidence estimation, out-of-vocabulary word detection...
The presence of Lombard Effect in speech is proven to have severe effects on the performance of speech systems, especially speaker recognition. Varying kinds of Lombard speech are produced by speakers under influence of varying noise types [1]. This study proposes a high-accuracy classifier using deep neural networks for detecting various kinds of Lombard speech against neutral speech, independent...
Recent studies have demonstrated the potential of unsupervised feature learning for sound classification. In this paper we further explore the application of the spherical k-means algorithm for feature learning from audio signals, here in the domain of urban sound classification. Spherical k-means is a relatively simple technique that has recently been shown to be competitive with other more complex...
A large number of extreme floods were closely related to heavy precipitation which lasted for several days or weeks. Long-lead prediction of extreme precipitation, i.e., prediction of 6–15 days ahead of time, is important for understanding the prognostic forecasting potential of many natural disasters, such as floods. Yet, long-lead flood forecasting is a challenging task due to the cascaded uncertainty...
Class imbalance problem refers to unequal distribution of data instances between classes. Due to this, popular classifiers misclassify data instances of minority class into majority class. Initially, Extreme learning machine was proposed with the prime objective of handling real valued datasets. Though, it a fast learning technique, it suffers from the drawback of misclassification of imbalanced dataset...
KNN is amongst the simplest top ten classification algorithm of data mining. Being effective and efficient it has some drawbacks which cannot be overlooked. Moreover, real world data is fuzzy in nature. To overcome this drawback fuzzy KNN was introduced which was based on fuzzy membership. But, it had large time complexity as the membership is calculated at the classification period. To improve this,...
To realize smart homes with sophisticated services including energy-saving context-aware appliance control in homes and elderly monitoring systems, automatic recognition of human activities in homes is essential. Several daily activity recognition methods have been proposed so far, but most of them still have issues to be solved such as high deployment cost due to many sensors and/or violation of...
For extorting the helpful comprehension concealed in the biggest compilation of a database the data mining technology is used. There are some negative approaches occurred about the data mining technology, among which the potential privacy incursion and potential discrimination. The latter consists of irrationally considering individuals on the source of their fitting to an exact group. Data mining...
This paper introduces a novel tree induction algorithm called sequential Random Forest (sRF) to improve the detection accuracy of a standard Random Forest classifier. Observations have shown that the overall performance of a forest is strongly influenced by the number of training samples. The main idea is to sequentially adapt the number of training samples per class so that each tree better complements...
In this paper, we describe a classifier based retrieval scheme for efficiently and accurately retrieving relevant documents. We use SVM classifiers for word retrieval, and argue that the classifier based solutions can be superior to the OCR based solutions in many practical situations. We overcome the practical limitations of the classifier based solution in terms of limited vocabulary support, and...
In an aging society, a service robot will come into our life. It is important for a robot to identify an object specified by human speech from several objects. Human may request an object for the robot by its name, and/or color name etc. Although there are some research about the method for the object identification based on its name, the object identification based on its color is not discussed enough...
P300-speller is a communication style based on Brain-computer interface (BCI) which allows users to input characters by electroencephalography (EEG) signals. In the past few years, there are various studies on P300-speller paradigm and classification algorithm. However, the accuracy and bit rates are not yet satisfied for our daily life. In order to improve the performance of the P300-speller, we...
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