11658624
Statistical Feature Selection for Mandarin Speech Emotion Recognition
Performance of speech emotion recognition largely depends on the acoustic features used in a classifier. This paper studies the statistical feature selection problem in Mandarin speech emotion recognition. This study was based on a speaker dependent emotional mandarin database. Pitch, energy, duration, formant related features and some velocity information were selected as base features. Some statistics of them consisted of original feature set and full stepwise discriminant analysis (SDA) was employed to select extracted features. The results of feature selection were evaluated through a LDA based classifier. Experiment results indicate that pitch, log energy, speed and 1st formant are the most important factors and the accuracy rate increases from 63.1 % to 76.5 % after feature selection. Meanwhile, the features selected by SDA are better than the results of other feature selection methods in a LDA based classifier and SVM. The best performance is achieved when the feature number is in the range of 9 to 12.
14850996
Effects of network characteristics on task performance in a desktop CVE system
The influences of network characteristics on the task performance in a desktop collaborative virtual environment (CVE) system were studied. In the experiment, two participants worked together to accomplish a collaborative task and a network emulator was utilized to simulate real network characteristics. The task used in the experiment was designed to be highly interactive and had rigid requirements on network characteristics (e.g. latency and packet loss). The objective performance and subjective assessment were recorded and analyzed quantitatively as a function of network latency and packet loss rate. The experiment results show that network latency has no distinct effect on task performance when network latency is less than 200 ms and lightly affect task performance when network latency is between 200 and 600 ms; packet loss has no effect on task performance when there are 40 state update messages received per second and lightly affect task performance when there are between 15 and 40 state update messages received per second.
206977606
Priority Based Overlay Multicast for Distributed Interactive Applications
Distributed Interactive Applications (DIAs) is different with other network-based applications, since the data should be delivered to the receiver node according to its priority. Among the existing overlay multicast protocols the "priority-based directed minimum spanning tree" (PST) is designed for DIAs. But in PST the available bandwidth (avail-bw) is used inefficiently and the system becomes unstable when its scale increases. This paper proposed a novel overlay multicast protocol named Priority Based Overlay Multicast (POM), which uses the entities’ priority, links’ delay and node’s avail-bw to build multicast tree. By using POM the nodes with high priority receive the data in short delay, while the nodes with low priority tolerate a long delay in order to balance the available bandwidth usage among the nodes. The simulation results show that POM uses the avail-bw efficiently and keeps the system stable when its scale increases. Keywords: DIAs; Priority; Available bandwidth.
8895078
An Adaptive-Filtering Policy for Distributed Virtual Environments
In this paper, an adaptive-filtering policy for distributed virtual environments (DVEs) was brought forward. It took advantages of two traditional filtering policies (static-filtering policy and equidistance-transmission policy) and could adaptively select the fittest filtering policy corresponding to current motion characteristic of the virtual object in DVEs. An experiment was designed to compare this filtering policy with the two traditional filtering policies on task performance. Objective (task completion time, state updates transmitted and state updates per second) and subjective (filtering quality) evaluations were recorded in the experiment. The experiment results show that if mean state updates per second is equal or greater than 20 in the task, SUT and SUPS can be decreased by adaptive-filtering policy without task performance affected obviously. However, if mean state updates per second is around 10 in the task, accompanied with SUT and SUPS decreased by adaptive-filtering policy, task performance also deteriorated badly
13928372
VRules: an effective association-based classifier for videos
Video classification is an important step towards multimedia understanding. Most state-of-the-art approaches which apply HMM to capture the temporal information of videos have the limitation by assuming that the current state of a video depends only on the immediate previous state. Nevertheless, this assumption may not hold for videos of various categories. In this paper, we present an effective video classifier which employs the association rule mining technique to discover the actual dependence relationship between video states. The discriminatory state transition patterns mined from different video categories are then used to perform classification. Besides capturing the association between states in the time space, we also capture the association between low-level features in spatial dimension to further distinguish the semantics of videos. Experimental results show that the performance of our association rule based classifier is quite promising.
10243856
A new LDA-based face recognition system which can solve the small sample size problem
A new LDA-based face recognition system is presented in this paper. Linear discriminant analysis (LDA) is one of the most popular linear projection techniques for feature extraction. The major drawback of applying LDA is that it may encounter the small sample size problem. In this paper, we propose a new LDA-based technique which can solve the small sample size problem. We also prove that the most expressive vectors derived in the null space of the within-class scatter matrix using principal component analysis (PCA) are equal to the optimal discriminant vectors derived in the original space using LDA. The experimental results show that the new LDA process improves the performance of a face recognition system signi"cantly. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
8697404
Audio streaming over Bluetooth: an adaptive ARQ timeout approach
Streaming audio has become a popular form of media on the Internet. As wireless personal area network architectures (e.g. Bluetooth) are now targeted to support multimedia traffic, streaming audio over these technologies give rise to interesting applications. But, the variable nature of the wireless medium will not lend itself easily to supporting audio streaming. We focus on Bluetooth and propose, an enhancement to the Bluetooth link layer ARQ mechanism to compensate for channel degradation and to better support audio streaming. Specifically, our scheme adoptively sets the ARQ timeout value based on current channel measurements. We show through testbed experiments that the adaptive ARQ improves the streaming quality significantly compared to the "vanilla" link layer of Bluetooth, especially in noisy environments. Our proposed approach is simple to implement and can actually be extended to the link layer of any wireless technology.
120801
Recognition of Motor Imagery Electroencephalography Using Independent Component Analysis and Machine Classifiers
Motor imagery electroencephalography (EEG), which embodies cortical potentials during mental simulation of left or right finger lifting tasks, can be used to provide neural input signals to activate a brain computer interface (BCI). The effectiveness of such an EEG-based BCI system relies on two indispensable components: distinguishable patterns of brain signals and accurate classifiers. This work aims to extract two reliable neural features, termed contralateral and ipsilateral rebound maps, by removing artifacts from motor imagery EEG based on independent component analysis (ICA), and to employ four classifiers to investigate the efficacy of rebound maps. Results demonstrate that, with the use of ICA, recognition rates for four classifiers (fisher linear discriminant (FLD), back-propagation neural network (BP-NN), radial-basis function neural network (RBF-NN), and support vector machine (SVM)) improved significantly, from 54%, 54%, 57% and 55% to 70.5%, 75.5%, 76.5% and 77.3%, respectively. In addition, the areas under the receiver operating characteristics (ROC) curve, which assess the quality of classification over a wide range of misclassification costs, also improved from .65, .60, .62, and .64 to .74, .76, .80 and .81, respectively.
11672412
Dealing with Node Mobility in Ad Hoc Wireless Network
A Mobile “Ad hoc” wireless NETwork (MANET) is a network established for a special, often extemporaneous service customized to applications. The ad hoc network is typically set up for a limited period of time, in an environment that may change from application to application. As a difference from the Internet where the TCP/IP protocol suite supports a vast range of applications, in the MANET the protocols are tuned to a specific customer and application (eg, send a video stream across the battlefield; find out if there is a fire in the forest; establish a videoconference among several teams engaged in a rescue effort, etc). The customers move and the environment may change dynamically and unpredictably. For the MANET to retain its efficiency, the ad hoc protocols at various layers may need to self-tune to adjust to environment, traffic and mission changes. From these properties emerges the vision of the MANET as an extremely flexible, malleable and yet robust and formidable network architecture. Indeed, an architecture that can be deployed to monitor the habits of birds in their natural habitat, and which, in other circumstances, can be organized to interconnect rescue crews after a Tsunami disaster, or yet can be structured to launch deadly attacks onto unsuspecting enemies.