This paper presents a study on the performance of the higher-order moments for speech/music classification. For a successful speech/music classifier, extracting features that allow direct access to the relevant speech or music specific information is crucial. In addition to the conventional variance-based features, we utilize the higher-order moments of features, such as skewness and kurtosis. Moreover, we investigate the subband decomposition parameters in extracting features, which improves classification accuracy. Experiments on two speech/music datasets, which are publicly available, were performed and show that the higher-order moment features can improve classification accuracy when combined with the conventional variance-based features.

Language is an essential tool for verbal and emotional communication among human beings, enabling them to engage in social interactions. Although a majority of hearing-impaired people can speak; however, they are unable to receive feedback on their pronunciation most of them can speak. However, they do not receive feedback on their pronunciation. This results in impaired communication owing to incorrect pronunciation, which causes difficulties in their social interactions. If hearing-impaired people could receive continuous feedback on their pronunciation and phonation through lip-reading training, they could communicate more effectively with people without hearing disabilities, anytime and anywhere, without the use of sign language. In this study, the mouth area is detected from videos of learners speaking monosyllabic words. The grayscale information of the detected mouth area is used to estimate a velocity vector using Optical Flow. This information is then quantified as feature values to classify vowels. Subsequently, a system is proposed that classifies monosyllables by algebraic computation of geometric feature values of lips using the characteristics of articulatory phonation. Additionally, the system provides feedback by evaluating the comparison between the information which is obtained from the sample categories and experimental results.

In this paper, for recognize a night pedestrian from an infrared video, a new method differentiated from the existing feature vector is proposed and experimented. The new approach focuses on the shape feature vector of the structure and shape of the pedestrian image divided by the human body seven split ratio. The pedestrian images are divided into 7 square blocks from the still image of the preprocessing process. And to reduce the dimension, the square block is converted into a mosaic block. The scalar and direction of the shape feature vector is calculated by the brightness and position of the element in the mosaic. For practicality of infrared video system, the proposed method simplifies the data to be processed by reducing the amount of data in the preprocessing in order to continuously batch process the entire system in real time. Through the experiments, we verified the validity of the proposed shape feature vector. In comparison to the existing method, we propose a new shape feature vector generation method as the feature vector for night pedestrian recognition.

In this paper, we propose a high-quality image acquisition algorithm using only a single image, which the high-quality image is normally referred as HDR ones. In order to acquire the HDR image, conventional methods need many images having different exposure values at the same scene and should delicately adjust the color values for a bit-expansion or an exposure fusion. Thus, they require considerable calculations and complex structures. Therefore, the proposed algorithm suggests a completely new approach using one image for the high-quality image acquisition by applying statistical difference and histogram manipulation, or histogram specification, techniques. The techniques could control the pixel's statistical distribution of the input image into the desired one through the local and the global modifications, respectively. As the result, the quality of the proposed algorithm is better than those of conventional methods implemented in commercial image editing softwares.

We have previously proposed a preliminary cut-off indoor positioning scheme considering the reference point with the same signal similarity. This scheme estimates the position using the relative rank of the peak of received signal strength from the beacons around user. However, this scheme has a weak point with lower accuracy when there are more than one nearest reference points having the same signal similarity. In order to tackle this, we propose a weighted preliminary cut-off indoor positioning scheme. Firstly, if the above problem occurs, the similarity to the peak of signal strength is considered as well as the relative rank. Next, weights are assigned to the nearest reference points using the similarity to the peak of the received signal strength. Finally, the user's position is estimated by applying the weights. As a result, the weighted preliminary cut-off scheme improves the positioning accuracy by about 7.9% compared to the previous scheme.

In this paper, we are applied a nerve network to allow for the reflection of data learning methods in their overall forms by using cluster data rather than data learning by the stages and then selected a nerve network model and analyzed its variables through learning by the cluster. The CkLR algorithm was proposed to analyze the reaction variables of clustering outcomes through an approach to the initialization of K-means clustering and build a model to assess the prediction rate of clustering and the accuracy rate of prediction in case of new data inputs. The performance evaluation results show that the accuracy rate of test data by the class was over 92%, which was the mean accuracy rate of the entire test data, thus confirming the advantages of a specialized structure found in the proposed learning nerve network by the class.

We propose a new framework which expresses the mist and scratches of cracked ice by an impact. We combine the grid projection technique, boundary particles method, and level-set method commonly used in fluid simulations to determine the region on the surface of an ice object which is affected by a collision. Mist is then generated in proportion to the impact, and immediately diffused, using a geodesic distance field to limit dissipation. The gradient of the mist is subsequently used to create realistic patterns of scratches and elongated air bubbles. Cracks of the ice object can also be considered, and the density of the mist made to vary realistically between fragments. As a result, our method not only represents high-quality ice effects, but also allows easy integration into existing rigid body simulation solvers.

In these days, various technologies to provide a service of high quality of 360 VR media contents is being studied and developed. However, rendering high-quality of media images is very difficult with the limited resources of HMD (Head Mount Display). In this paper, we designed and implemented a 360 VR Player for high quality 360 tiled video image render to HMD. Furthermore, we developed multi-resolution-based Foveated Rendering technology. By conducting several experiments, We have confirmed that it improved the performance of video rendering far more than existing tiled video rendering technology.

Recently, there are several active and vibrant studies on Realistic Media that try to provide the immersion and the presence by adding sensory effects to video. Because existing sensory effects on Realistic Media are produced through experts and automation programs on sensory effects, there is a limit to fill up the gap of user satisfaction which comes from the diversity of cybernauts. In this paper, we propose a method to improve the satisfaction with sensory effects by collecting and analyzing user's response information, and applying the result to the attributes of sensory effects. The proposed method allows you to produce the Sensory Effect Metadata effectively by analyzing users' biometric data and subjective satisfaction with realistic media experience, measuring the weighted value to revise the effects, and applying to the attribute of sensory effect. The experimental result shows that the proposed method improves the satisfaction with realistic content by generating the revised Sensory Effect Metadata in response to user's experiential feedback information.