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Binaural features of interaural level difference and interaural phase difference have proved to be very effective in training deep neural networks (DNNs), to generate time-frequency masks for target speech extraction in speech-speech mixtures. However, effectiveness of binaural features is reduced in more common speech-noise scenarios, since the noise may over-shadow the speech in adverse conditions...
Many early stage lung cancer patients have resectable tumors, however, their cardiopulmonary function needs to be properly evaluated before they are deemed operative candidates. Such patients are typically asked to undergo standard pulmonary function tests, including cardiopulmonary exercise tests (CPET) or stair climbs. The standard tests are conducted only at selected healthcare provider locations,...
This paper targets on a generalized vocal mode classifier (speech/singing) that works on audio data from an arbitrary data source. Previous studies on sound classification are commonly based on cross-validation using a single dataset, without considering training-recognition mismatch. In our study, two experimental setups are used: matched training-recognition condition and mismatched training-recognition...
Smartphones have become the pervasive personal computing platform. Recent years thus have witnessed exponential growth in research and development for secure and usable authentication schemes for smartphones. Several explicit (e.g., PIN-based) and/or implicit (e.g., biometrics-based) authentication methods have been designed and published in the literature. In fact, some of them have been embedded...
New and unseen network attacks pose a great threat to the signature-based detection systems. Consequently, machine learning-based approaches are designed to detect attacks, which rely on features extracted from network data. The problem is caused by different distribution of features in the training and testing datasets, which affects the performance of the learned models. Moreover, generating labeled...
Hairstyle recognition is a challenging task since hairstyles span a diverse range of appearances in real-world. However, it is possible to start from recognizing the most basic hairstyles then dealing with more complex hairstyles. In this paper, we present a novel hairstyle pattern recognition system based on CNNs. We first give the definitions of four basic hairstyles: straight hairstyle, curly hairstyle,...
Action recognition is still a challenging problem. In order to catch effective compact representation of the action sequences, the discriminative dictionaries could be learned by sparse coding. But sparse coding is needed in both the training and testing phases of the classifier framework. And it is also time consuming for the adoption of 1-norm sparsity constraint on the representation coefficients...
Authorship recognition from micro-blogs such as Twitter is a challenging task due to limitation of text length to 140 characters. However, identification of micro-blog authors is crucial in many cyber-crime investigations as well as in forensic applications. So far, traditional linguistic profiles such as Bag-Of-Words (BOW) and style-based markers have been investigated for identification of micro-blog...
Machine learning has become one of the go-to methods for solving problems in the field of networking. This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks. While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when...
The enhancement of speech degraded with the non-stationary noise types that typify real-world conditions has remained a challenging problem for several decades. However, recent use of data driven methods for this task has brought great performance improvements. In this paper, we develop a speech enhancement framework based on the extreme learning machine. Experimental results show that the proposed...
This paper presents a novel approach for remaining useful life (RUL) prediction of rotating machinery using hierarchical deep neural networks (DNN). The different health stages are classified by a DNN-based health stage classifier trained by segmented degradation signal. This method builds several RUL predictors based on the health stages of the degradation process. Instead of modeling the entire...
Recently, sparse representation based classifiers (SRC) and collaborative representation based classifiers (CRC) have been shown to give very good performance under controlled scenarios. However, in practical applications, face recognition often encounters variations in illumination, expression, noise and occlusion, which cause severe performance degradation (due to the outliers in testing). In this...
Use of online applications in day-to-day life is increasing. In parallel to this increase the threat to the security of these applications is also increasing. The security of these applications is breached by different cyber attacks. Denial-of-Service (DoS) is one such type of cyber attack. DoS makes the online application or the resources of the server unavailable to the intended users. For detecting...
Upper limb disorders may impair the use of control interfaces for Electric Powered Wheelchairs (EPW), such as joysticks, for many individuals with disabilities. The aims of this study were to develop and test a virtual wheelchair driving environment that can provide quantifiable measures of driving ability, offer driver training, and measure the performance of alternative controls. This work introduces...
The SFSVC (Super Fast Support Vector Classifier) architecture is implemented to a computational mobile platform and its performances are evaluated against its implementation on a classic machine (personal computer). The aim of this article is to prove that the SFSVC architecture can have good performances on an environment with very limited resources by taking advantages of its compact structure and...
In this paper we shall present recent results of two applications for monitoring using acoustical signal classification. The first case study is the problem of context awareness based on acoustic analysis for a service robot. Then we discussed the acoustic classification for wildlife intruder detection. Previous results are briefly recalled and new experimental results are also provided.
Based on the method of Skeletonization, the concept of influence factor is introduced in this paper. A method for trimming the fat from a Back Propagation (BP) neural network is proposed by modifying weight and influence factor alternately, and node with the least influence factor was deleted. This method is applied to modeling superheated steam temperature system of plant station. Simulation results...
Although Deep Convolutional Neural Networks (CNNs) have liberated their power in various computer vision tasks, the most important components of CNN, convolutional layers and fully connected layers, are still limited to linear transformations. In this paper, we propose a novel Factorized Bilinear (FB) layer to model the pairwise feature interactions by considering the quadratic terms in the transformations...
Cascade is a widely used approach that rejects obvious negative samples at early stages for learning better classifier and faster inference. This paper presents chained cascade network (CC-Net). In this CC-Net, there are many cascade stages. Preceding cascade stages are placed at shallow layers. Easy hard examples are rejected at shallow layers so that the computation for deeper or wider layers is...
Deep Convolutional Neural Networks (CNNs) achieve substantial improvements in face detection in the wild. Classical CNN-based face detection methods simply stack successive layers of filters where an input sample should pass through all layers before reaching a face/non-face decision. Inspired by the fact that for face detection, filters in deeper layers can discriminate between difficult face/non-face...
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