Search

Cardiotocography (CTG) is a clinical procedure that is used to track and gauge the severity of fetal distress. Although CTG is the most often used equipment to monitor and assess the health of the fetus, the high rate of false positive results due to visual interpretation significantly contributes to needless surgical delivery or delayed intervention. In this study, a novel approach is introduced where both printing CTG paper is digitized and a machine learning approach is employed to detect the abnormality in the digitized CTG signal. Image processing-based preprocessing steps are employed to make the printing of CTG paper more convenient to extract the CTG signal. Various signal-processing techniques are used to calibrate the extracted CTG signal. Then, Empirical Mode Decomposition (EMD) is used to decompose the CTG signal into its frequency components and instantaneous frequency and spectral entropy features are extracted. After feature normalization and feature selection with ReliefF algorithm, support vector machines (SVM) is used for the classification of the normal and abnormal classes. A novel dataset is used in the experimental works and various performance evaluation metrics are used for the evaluation of the achievement of the proposed method. 10-fold cross-validation-based experiments show that the proposed method is quite efficient in abnormality detection in printing CTG papers where an average accuracy score of around 90.0% is produced.

The robotic wearable exoskeletons have been developed due to various advantages offered by these devices. These advantages are manifested by integrating the human and a robot into one system under the user's control, which motivated researchers to develop different exoskeletons through years. However, several advances in exoskeleton; still, dealing with the various technical challenges in designing these impressive devices is inevitably. This paper aims to introduce informative resources and quick guidance of various technical challenges as such information is critical for exoskeleton development. The constructive discussion is intended to encourage researchers, innovators and academia to be aware of these challenges. Finally, the contemporary research gaps with various challenges have been highlighted, which remain to be solved as well as some future directions in this field that will have far-reaching effects on developing exoskeletons.

We demonstrate power-scaling of nonlinear frequency conversion in fibres via dispersive wave generation using higher order modes. By pumping in the LP0,m modes at 1030 nm we show wavelength selectability between 550 and 800 nm with pulse energies up to tens of nJ.

In the past two decades, there has been a consider-able increase in use of computer-based exams. Currently there is a lack of comprehensive understanding about equivalence between paper-based and computer-based exams in engineering and computing education. This study utilised a systematic literature review (using PRISMA) to synthesise the literature regarding paper and computer-based testing in engineering and computing education between 2010-2020 (inclusive). The research database that was used was Scopus. Publications were first screened by title, abstract and keywords. This was followed by screening the full text of each publication. Twenty-four papers were included in the final analysis. Outcomes demonstrated no clear result regarding equivalency between computer-based and paper-based exams. Similarly, many studies reported that students preferred computer-based exams, but this was not ubiquitous. Educators should consider these factors when considering transitioning from paper to computer-based testing.

Theodolite is a framework for benchmarking the scalability of cloud-native applications. It automates deployment and monitoring of a cloud-native application for different load intensities and provisioned cloud resources and assesses whether specified service level objectives (SLOs) are fulfilled. Provided as a Kubernetes Operator, Theodolite allows defining, sharing, and archiving benchmarks and experiment configurations in declarative files. We demonstrate Theodolite’s benchmarking method and show how researchers and cloud engineers can execute existing scalability benchmarks or define new ones with Theodolite.

Theodolite is a framework for benchmarking the scalability of cloud-native applications. It automates deployment and monitoring of a cloud-native application for different load intensities and provisioned cloud resources and assesses whether specified service level objectives (SLOs) are fulfilled. Provided as a Kubernetes Operator, Theodolite allows defining, sharing, and archiving benchmarks and experiment configurations in declarative files. We demonstrate Theodolite’s benchmarking method and show how researchers and cloud engineers can execute existing scalability benchmarks or define new ones with Theodolite.

In this paper, we study path planning algorithms of resource constrained mobile agents in unknown cluttered environments, which include but are not limited to various terrestrial missions e.g., search and rescue missions by drones in jungles, and space missions e.g., navigation of rovers on the Moon. In particular, we focus our attention on artificial potential field (APF) based methods, in which the target is attractive while the obstacles are repulsive to the mobile agent. In this paper, we propose two major updates to the classical APF algorithm which significantly improve the performance of path planning using APF. First, we propose to improve an existing classical method that replaces the gradient descent optimization of the potential field cost function on a continuous domain with a combinatorial optimization on a set of predefined points (called bacteria points) around the agent's current location. Our proposition includes an adaptive hyperparameter that changes the value of the potential function associated to each bacteria point based on the current environmental measurements. Our proposed solution improves the navigation performance in terms of convergence to the target at the expense of minimal increase in computational complexity. Second, we propose an improved potential field cost function of the bacteria points by introducing a new branching cost function which further improves the navigation performance. The algorithms were tested on a set of Monte Carlo simulation trials where the environment changes for each trial. Our simulation results show 25% lower navigation time and around 300% higher success rate compared to the conventional potential field method, and we present future directions for research., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Signal Processing Systems

IEEE SA White Paper, The security aspects of V2V communications in UAS addressed by IEEE P1920.2 are delineated in this paper. Those include the architecture of communication interfaces, authentication of V2V parties, cryptographic key management, and zero trust architecture.

The 2nd workshop on Datasets for Developing Intelligent XR Applications (DATA4XR) aims to address the challenges of public datasets and reproducibility in Extended Reality, also known as XR (Augmented Reality, Virtual Reality, and Mixed Reality) research. The workshop brings together experts to discuss the availability, privacy concerns, and ethics related to open-sourcing the datasets used in XR research for algorithm training and user behavior analysis. By examining the ethical, moral, and privacy concerns and categorizing open-source datasets for XR, the workshop expects to advance XR research by incorporating artificial intelligence and defining a research agenda for XR in open science. In addition, the workshop focuses on involving the Human-Computer Interaction (HCI) community in exploring its role in the future of XR research. We invite submissions of technical papers, position papers, and research papers on topics related to XR and open science.

So far, the relationship between open science and software engineering expertise has largely focused on the open release of software engineering research insights and reproducible artifacts, in the form of open-access papers, open data, and open-source tools and libraries. In this position paper, we draw attention to another perspective: scientific insight itself is a complex and collaborative artifact under continuous development and in need of continuous quality assurance, and as such, has many parallels to software artifacts. Considering current calls for more open, collaborative and reproducible science; increasing demands for public accountability on matters of scientific integrity and credibility; methodological challenges coming with transdisciplinary science; political and communication tensions when scientific insight on societally relevant topics is to be translated to policy; and struggles to incentivize and reward academics who truly want to move into these directions beyond traditional publishing habits and cultures, we make the parallels between the emerging open science requirements and concepts already well-known in (open-source) software engineering research more explicit. We argue that the societal impact of software engineering expertise can reach far beyond the software engineering research community, and call upon the community members to proactively help driving the necessary systems and cultural changes towards more open and accountable research., Multimedia Computing

This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small., Funding: Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems (VINNOVA); Saab AB

Stock investment is a low barrier but high-risk approach of investing, which requires lots of knowledge and experience for investors in order to earn money. The work in this paper enables investors who do not meet the above conditions to still make accurate investment actions with the predicted output given by the model. In this paper, ten years of historical data of Amazon was used to train the LSTM model, then different candidates of model architecture were attempted to test out the optimal one for predicting the stock close price of Amazon and evaluated. It was observed in the result that the model with more neurons in each hidden layers and less LSTM layers and Dense layers showed a higher accuracy and a more stable forecast. In the other hand, the numbers of historical data memorized did matter. The less memory days could result in a smaller mean absolute percentage error. © 2023 IEEE.

This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small., Funding: Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems (VINNOVA); Saab AB

This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small., Funding: Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems (VINNOVA); Saab AB

This paper considers fusion of dimension-reduced estimates in a decentralized sensor network. The benefits of a decentralized sensor network include modularity, robustness and flexibility. Moreover, since preprocessed data is exchanged between the agents it allows for reduced communication. Nevertheless, in certain applications the communication load is required to be reduced even further. One way to decrease the communication load is to exchange dimension-reduced estimates instead of full estimates. Previous work on this topic assumes global availability of covariance matrices, an assumption which is not realistic in decentralized applications. Hence, in this paper we consider the problem of deriving dimension-reduced estimates using only local information. The proposed solution is based on an estimate of the information common to the network. This common information estimate is computed locally at each agent by fusion of all information that is either received or transmitted by that agent. It is shown how the common information estimate is utilized for fusion of dimension-reduced estimates using two well-known fusion methods: the Kalman fuser which is optimal under the assumption of uncorrelated estimates, and covariance intersection. One main theoretical result is that the common information estimate allows for a decorrelation procedure such that uncorrelated estimates can be maintained. This property is crucial to be able to use the Kalman fuser without double counting of information. A numerical comparison suggests that the performance degradation of using the common information estimate, compared to having local access to the actual covariance matrices computed by other agents, is relatively small., Funding: Industry Excellence Center LINK-SIC - Swedish Governmental Agency for Innovation Systems (VINNOVA); Saab AB

We propose a new blind watermarking algorithm for three-dimensional (3D) printed objects that has applications in metadata embedding, robotic grasping, counterfeit prevention, and crime investigation. Our method can be used on fused deposition modeling (FDM) 3D printers and works by modifying the printed layer thickness on small patches of the surface of an object. These patches can be applied to multiple regions of the object, thereby making it resistant to various attacks such as cropping, local deformation, local surface degradation, or printing errors. The novelties of our method are the use of the thickness of printed layers as a one-dimensional carrier signal to embed data, the minimization of distortion by only modifying the layers locally, and one-shot detection using a common paper scanner. To correct encoding or decoding errors, our method combines multiple patches and uses a two-dimensional (2D) parity check to estimate the error probability of each bit to obtain a higher correction rate than a naive majority vote. The parity bits included in the patches have a double purpose because, in addition to error detection, they are also used to identify the orientation of the patches. In our experiments, we successfully embedded a watermark into flat surfaces of 3D objects with various filament colors using a standard FDM 3D printer, extracted it using a common 2D paper scanner and evaluated the sensitivity to surface degradation and signal amplitude.

This paper proposes a novel method for generating arbitrary stereoscopic view from multiple omni directional image sequences. Although conventional methods for arbitrary view generation with an image-based rendering approach can create binocular views, positions and directions of viewpoints for stereoscopic vision are limited to a small range. In this research, we attempt to generate arbitrary stereoscopic views from omni directional image sequences that are captured in various multiple paths. To generate a high-quality stereoscopic view from a number of images captured at various viewpoints, appropriate ray information needs to be selected. In this paper, appropriate ray information is selected from a number of omni directional images using a penalty function expressed as ray similarity. In experiments, we show the validity of this penalty function by generating stereoscopic view from multiple real image sequences., ICPR 2010 : 20th International Conference on Pattern Recognition , Aug 23-26, 2010 , Istanbul, Turkey

In this paper, a barge-in free spoken dialogue system using sound field control and microphone array is proposed. In the conventional spoken dialogue system using an acoustic echo canceller, it is indispensable to estimate and update the room transfer function, especially when the transfer function is changed by various interferences. However the estimation process for the transfer function prevents the user from speaking freely and simultaneously with speech responses from the system. In order to resolve the problem, we have already proposed a barge-in free spoken dialogue system that controls a sound field using multiple loudspeakers. In this paper, a microphone array for acquisition of user's speech is newly introduced in the previously proposed system. By introducing the microphone array, we can reduce the number of loudspeakers to be required in the system, and make the interface for spoken dialogue system more robust against the change of room transfer functions., ICASSP2003: IEEE International Conference on Acoustics, Speech, and Signal Processing, April 6-10, 2003, Hong Kong, China.

This paper describes a new blind signal separation method using the directivity patterns of a microphone array. In this method, to deal with the arriving lags among each microphone, the inverses of the mixing matrices are calculated in the frequency domain so that the separated signals are mutually independent. Since the calculations are carried out in each frequency independently, the following problems arise: (1) permutation of each sound source, (2) arbitrariness of each source gain. In this paper, we propose a new solution that directivity patterns are explicitly used to estimate each sound source direction. As the results of signal separation experiments, it is shown that the proposed method improves the SNR of degraded speech by about 16 dB under non-reverberant condition. Also, the proposed method improves the SNR by 8.7 dB when the reverberation time is 184 ms, and by 5.1 dB when the reverberation time is 322 ms, ICASSP2000: IEEE International Conference on Acoustics, Speech, and Signal Processing, June 5-9, 2000, Istanbul, Turkey.

Deals with the recognition of distant talking speech and, particularly, with the simultaneous recognition of multiple sound sources. A problem that must be solved in the recognition of distant talking speech is talker localization. In some approaches, the talker is localized by using short- and long-term power. The 3-D Viterbi search based method proposed by Yamada et al.(1998), integrates talker localization and speech recognition. This method provides high recognition rates but its application is restricted to the presence of one talker. In order to deal with multiple talkers, we extended the 3-D Viterbi search method to a 3-D N-best search method enabling the recognition of multiple sound sources. The paper describes our baseline 3-D N-best search-based system and two additional techniques, namely, a likelihood normalization technique and a path distance-based clustering technique. The paper also describes experiments carried out in order to evaluate the performance of the system, ICASSP2001: IEEE International Conference on Acoustics, Speech and Signal Processing, May 7-11, 2001, Salt Lake City, Utah, US.

This paper describes a method for determining the vocal tract spectrum from articulatory movements using an hidden Markov models (HMMs). In the proposed system, articulatory parameters are generated from a TTS system and converted to acoustic features to be synthesized. Comparing with conventional GMM-based systems, the proposed system has two additional properties: 1) phonetic information given input texts is available for the conversion, 2) the use of HMMs allows us to utilize the temporal structure of speech. In this paper, we investigate the optimal structure of HMMs for the conversion. Experimental results show that using phonetic and temporal information can improve the mapping accuracy in a spectral distortion measure, ICASSP2006: IEEE International Conference on Acoustics, Speech, and Signal Processing, May 14-19, 2006, Toulouse, France.

In this paper, we reveal new findings about the generated musical noise in minimum mean-square error short-time spectral amplitude (MMSE STSA) processing. Recently we have proposed a objective metric of musical noise based on kurtosis change ratio on spectral subtraction (SS). Also we found an interesting relationship among the degree of generated musical noise, the shapes of signal-s probability density function, the strength parameter of SS processing. This paper is aimed to automatically evaluate the sound quality of various types of noise reduction methods using kurtosis change ratio. We give a mathematical analysis based on higher-order statistics viewpoint, and lead to a valuable relation in that MMSE STSA has a weakness in speech period distortion rather than noise period, and vice versa in SS., ICASSP2009: IEEE International Conference on Acoustics, Speech, and Signal Processing, April 19-24, 2009, Taipei, Taiwan.

The performance of a speech recognizer is degraded drastically in reverberant environments. The authors propose a novel algorithm which can model an observation signal by composition of HMMs of clean speech, noise and an acoustic transfer function. However, estimating HMM parameters of the acoustic transfer function is still a serious problem. In their previous paper, they measured real impulse responses of training positions in an experiment room. It is inconvenient and unrealistic to measure impulse responses for every possible new experiment room. The paper presents a new method for estimating HMM parameters of the acoustic transfer function from some adaptation data by using an HMM decomposition algorithm which is an inverse process of the HMM composition. Its effectiveness is confirmed by a series of speaker dependent and independent word recognition experiments on simulated distant-talking speech data, ICASSP1997: IEEE International Conference on Acoustics, Speech, and Signal Processing, April 21-24, 1997.

This study is concerned with a large-scale telepresence system based on remote control of mobile robot or aerial vehicle. The proposed system provides a user with not only view of remote site but also related information by AR technique. Such systems are referred to as augmented telepresence in this paper. Aerial imagery can capture a wider area at once than image capturing from the ground. However, it is difficult for a user to change position and direction of viewpoint freely because of the difficulty in remote control and limitation of hardware. To overcome these problems, the proposed system uses an autopilot airship to support changing user's viewpoint and employs an omni-directional camera for changing viewing direction easily. This paper describes hardware configuration for aerial imagery, an approach for overlaying virtual objects, and automatic control of the airship, as well as experimental results using a prototype system., ISMAR 2010 : 9th IEEE International Symposium on Mixed and Augmented Reality , Oct 13-16, 2010 , Seoul, Korea

This paper describes a hands-free speech recognition method based on HMM composition and separation for speech contaminated not only by additive noise but also by an acoustic transfer function. The method realizes an improved user interface such that a user is not encumbered by microphone equipment in noisy and reverberant environments. In this approach, an attempt is made to model acoustic transfer functions by means of an ergodic HMM. The states of this HMM correspond to different positions of the sound source. It can represent the positions of the sound sources, even if the speaker moves. The HMM parameters of the acoustic transfer function are estimated by HMM separation. The method is obtained through the reverse of the process of HMM composition, where the model parameters are estimated by maximizing the likelihood of adaptation data uttered from an unknown position. Therefore, measurement of impulse responses is not required. In this paper, we record the speech of a distant moving speaker in real environments. The results of experiments for the speech of a distant moving speaker clarified the effectiveness of HMM composition and separation, ICASSP2000: IEEE International Conference on Acoustics, Speech, and Signal Processing, June 5-9, 2000, Istanbul, Turkey.

In this paper we propose a new unsupervised training method for nonlinear spatial filter using a new independent component analysis based on kernel infomax. The nonlinearity of the spatial filter used in this paper is equivalent to the integration of beamforming and spectral subtraction, and the whole structure is optimized by independent component analysis in the reproducing kernel Hilbert space. The optimized filter is shown to be capable of achieving better quality output than the conventional method based on time-frequency binary masking., ICASSP2009: IEEE International Conference on Acoustics, Speech, and Signal Processing, April 19-24, 2009, Taipei, Taiwan.

This paper study the blind estimation of the diffuse background noise for the hands-free speech interface. Some recent papers showed that it is possible to use blind signal separation (BSS) to estimate the diffuse background noise by suppressing the speech component after all the components were separated. In particular, the scale indeterminacy of BSS is avoided by using the projection back method. In this paper, we study an alternative to the projection back for the noise estimation and justify the use of blind signal extraction BSE rather than BSS., ICASPP2010: IEEE International Conference on Acoustics, Speech, and Signal Processing, March 14-19, 2010, Dallas, Texas, USA.

This paper presents a novel method for training hidden Markov models (HMMs) for use in HMM-based speech synthesis. The primary goal of HMM parameter optimization is to ensure that parameters generated from the trained models exhibit similar properties to natural speech. In this paper, two major problems in conventional training are addressed: 1) the inconsistency between the training and synthesis optimization criterion; and 2) the over-smoothing caused by the statistical modeling process. The proposed method integrates the global variance (GV) criterion into a trajectory training method to give a unified framework for both training and synthesis which provides both a consistent optimization criterion and a closed form solution for parameter generation. The experimental results demonstrate that the proposed method yields a significant improvement in the naturalness of synthetic speech., ICASSP2009: IEEE International Conference on Acoustics, Speech, and Signal Processing, April 19-24, 2009, Taipei, Taiwan.

One of the common problems in building an effort estimation model is that not all the effort factors are suitable as predictor variables. As a supplement of missing information in estimation models, this paper explores the project manager's knowledge about the target project. We assume that the experts can judge the target project's productivity level based on his/her own expert knowledge about the project. We also assume that this judgment can be further improved, because using the expert's judgment solely could incur subjective perception. This paper proposes a regression model building/selection method to address this challenge. In the proposed method, a fit dataset for model building is divided into two or three subsets by project productivity, and an estimation model is built on each data subset. The expert judges the productivity level of the target project and selects one of the models to be used. In the experiment, we used three datasets to evaluate the produced effort estimation models. In the experiment, we adjusted the error rate of the judgment and analyzed the relationship between the error rate and the estimation accuracy. As a result, the judgment-incorporating models produced significantly higher estimation accuracy than the conventional linear regression model, where the expert's error rate is less than 37%., 2012 19th Asia-Pacific Software Engineering Conference, 4-7 Dec. 2012, Hong Kong, China

The sound data for open evaluation is necessary for studies such as sound source localization, sound retrieval, sound recognition and hands-free speech recognition in real acoustic environments. This paper reports on our project for acoustic data collection. There are many kinds of sound scenes in real environments. The sound scene is specified by sound sources and room acoustics. The number of combinations of the sound sources, source positions and rooms is huge in real acoustic environments. We assumed that the sound in the environments can be simulated by convolution of the isolated sound sources and impulse responses. As an isolated sound source, hundred kinds of environment sounds and speech sounds are collected. The impulse responses are collected in various acoustic environments. Additionally we collected sounds from a moving source. In this paper, progress of our sound scene database collection project and application to environment sound recognition and hands-free speech recognition are described., ICME2002: IEEE International Conference on Multimedia and Expo, August 27-29, 2002, Lusanne, Switzerland.

The paper describes a talking face generation system with natural and communicative reality. If face movements are synthesized well enough for natural communication, a lot of benefits will be brought for the human-machine communication. We have already proposed a speech driven HMM-based lip movement synthesis, and also shown that the quality is drastically improved by considering succeeding visemes. The paper describes extension of our system to full face movement generation, and proposes a method considering both of preceding and succeeding viseme contexts. The experiments show the proposed method generates natural and accurate talking faces from audio speech inputs, ICME2000: IEEE International Conference on Multimedia and Expo, July 30 - August 2, 2000, NewYork, USA.

It is not unreasonable to say that examples are one of the most commonly used knowledge sources when learning the usage and best practices of a new API. That being said, in many cases the examples provided on the APIs' Web site are lacking in quantity or quality, so developers have to resort to other information sources, namely blogs and coding forums. Moreover, there is no good way for API developers to measure anything concerning the examples they are creating. In order to resolve the problem of lacking examples information and feedback in their creation, our goal is to develop metrics for empirical measurement of examples and to offer support during the APIs example creation steps, and this paper represents the starting point towards that aim. We have analyzed the source code examples provided on the API's Web site or GitHub directory for seven popular API libraries written in Java and measured certain metrics, such as example coverage, example code to source code ratio, class coverage percentage, and problems that occur with the compilation and execution of existing examples. The purpose of this paper is to investigate the current situation of examples and provide a starting knowledge base for building an automatic tool for source code example metrics analysis., SANER 2016 : 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, 14-18 March 2016, Suita, Japan

The Takemaru-kun system is a real world speech-oriented guidance system located at the Ikoma-City North Community Center. The system has been operated daily from November, 2002, to provide visitors a speech interface for information retrieval. This system also aims at the field test of a speech interface and collecting actual utterance data. By analyzing and evaluating the collected utterances, the flexible processing requirements are discovered according to the user's age group. It becomes impossible to disregard the increase of child users when the system is installed in a public place. The paper proposes an automatic approach discriminating speakers between adult and child users, which is based on statistical learning. This proposal realizes a flexible spoken dialogue to both adult and child users. As for parameter vectors in machine learning, acoustic and linguistic properties extracted from speech recognition logarithm likelihood scores are adopted to discriminate a user's age group. Although GMM-based recognition uses only acoustic properties, this method can also consider linguistic properties. In experiments with SVM-based screening, we obtained a 92.4% discrimination rate to the actual users' utterances. The advantage of using linguistic properties is also shown. The paper also describes an overview of the Takemaru-kun system and the data collection status from the field test. Child speech recognition performance is evaluated using the collected utterances., ICASSP2004: IEEE International Conference on Acoustics, Speech, and Signal Processing, May 17-21, 2004, Quebec, Canada.

This paper describes a new method of measuring position of running user for location-based services in wide indoor environments, such as augmented reality navigation system with wearable computer. Conventional localization methods usually employ a hybrid approach in which user's position is estimated bv combining positioning infrastructures and a pedometer. Since the installation cost of infrastructures increases when the area expands, the measurement Figure Illustration summing up step method of user's relative position with high accuracy is required. Although a numder of methods using a pedometer have been developed to improve the estimation accuracy, these methods generally can handle only usual walking behavior of a user. This paper proposes a new real time localization method for both walking and running users by using a wearable electromagnetic tracker and an inertial sensor. The proposed localization method estimates a moving distance in the period when both legs do not ground by estimating a velocity of waist when user's leg leaves. Experiments have been carried out using a prototype system to evaluate the accuracy of user localization with the proposed method., ICAT 2007 : The 17th International Conference on Artificial Reality and Telexistence , Nov 28-30, 2007 , Esbjerg, Denmark

This paper describes a voice quality control method in statistical esophageal speech enhancement. Esophageal speech is produced by one of the alternative speaking methods for laryngectomees. Its naturalness and intelligibility are much lower than those of natural voices and its voice quality sounds similar even if uttered by different laryngectomees. These issues are alleviated by a statistical voice conversion method from esophageal speech into normal speech (ES-to-Speech) based on eigenvoices. This method is capable of determining converted voice quality using a few target voice samples. In this paper, we propose ES-to-Speech using regression techniques to make it possible to manually control the converted voice quality by manipulating a few intuitively controllable parameters even if no target voice sample is available. The effectiveness of the proposed method is confirmed by experimental evaluations., ICASSP2012: The 37th International Conference on Acoustics, Speech, and Signal Processing, March 25-30, Kyoto, Japan.

This paper describes a method for modeling wide area outdoor environments by integrating omnidirectional range and color images. The proposed method effectively reconstructs the 3D models of outdoor environments by using omnidirectional laser rangefinder and omnidirectional multi-camera system (OMS). In this paper, we also give experimental results of 3D wide area reconstruction using the data acquired at 50 points in our campus., ISMAR 2004 : 3rd IEEE and ACM International Symposium on Mixed and Augmented Reality , Nov 2-5, 2004 , Arlington, VA, USA

This paper addresses a noise-robust hands-free speech recognition system in which Single-Input Multiple-Output (SIMO)-model-based blind source separation (BSS) is introduced to achieve a fluent human-machine interaction. The method consists of SIMO-ICA technique and SIMO-model-based binary masking, which can achieve a superior noise reduction with low distortion. In this paper, we investigate the distortion controllability of the two-stage BSS method. The experimental results reveal that the speech recognition rate can be considerably improved by using the two-stage method compared with the conventional BSS methods. In addition, a BSS-based hands-free spoken dialogue system is illustrated., ISSPA2007: 9th International Symposium on Signal Processing and its Application, February 12-15, 2007, Sharjah, UAE.

This paper describes a localization method with wearable electromagnetic sensor and orientation sensor for wearable computer users. Many user localization methods have been investigated to realize location-based services in a wide environment. The localization methods usually employ a hybrid approach in which user's position is estimated by using positioning infrastructures and dead reckoning such as a pedometer. However, the installation cost of infrastructures increases when the area expands, and the error of the pedometer is frequently caused by failures in walking locomotion detection and the difference between ideal and estimated step lengths. If the relative distance is accurately estimated by dead reckoning approach, the installation cost of infrastructures can be reduced. This paper proposes a new localization method that improves the estimation accuracy of step length in dead reckoning approach. The proposed method measures user's orientation and geometrical relationship between user's heel and waist with an orientation sensor and an electromagnetic tracker attached to user's body. When both feet come into contact with the ground, user's position is updated by adding the estimated step length which means the relationship between user's heels and the position estimated in the previous step. The proposed localization method has been experimented using a prototype system to evaluate the accuracy of the proposed method., ISWC'06 : IEEE International Symposium on Wearable Computers , Oct 11-14, 2006 , Montreux, Switzerland

In this paper, we propose a new theoretical analysis of amount of musical noise generated in several noise reduction methods with a decision-directed a priori SNR estimator using higher-order statistics. In our previous study, a musical noise assessment based on kurtosis has been successfully applied to spectral subtraction and Wiener filter. However, this approach cannot be applied to some high-quality noise reduction methods, namely, the minimum mean-square error short-time spectral amplitude estimator, the minimum mean square error log-spectral amplitude estimator and the maximum a posteriori estimator, because such methods include the decision-directed a priori SNR estimator, which corresponds to a nonlinear recursive (infinite) process for noise power spectral sequences. Therefore, in this paper, we introduce a computationally efficient higher-order-moment calculation method based on generalized Gauss-Laguerre quadrature. We also mathematically clarify the justification of using a typical decision-directed parameter, namely, magic number 0.98, in the three types of the decision-directed-based estimators from a viewpoint of amounts of musical noise and speech distortion. In addition, we perform comparison between these noise reduction methods based on the mathematical analysis and human perception test., APSIPA ASC 2012 : Asia-Pacific Signal and Information Processing Association 2012 Annual Summit and Conference, December 3-6, 2012, Hollywood, California, USA.

In this paper, we propose an appropriate structure selection algorithm for less musical-noise generation in integration methods of microphone array and spectral subtraction. In our previous work, we have analyzed musical-noise reduction structure in integration methods of microphone array and spectral subtraction based on higher order statistics. However, that analysis can be applied to only Gaussian and super-Gaussian noises. In this paper, we extend the analysis into sub-Gaussian noise case. As a result of the extended analysis, we find the fact that an appropriate structure is depending on the type of input noise. Based on this fact, we propose an appropriate structure selection algorithm in integration methods of microphone array and spectral subtraction for less musical-noise generation. The effectiveness of the proposed algorithm are shown via a computer simulation., SSP2009: IEEE/SP 15th Workshop on Statistical Signal Processing, August 31 - September 3, 2009, Cardiff, UK.

The triage tag is used in Mass Casualty Incident (MCI) to check the priority of patients treatments and conditions. However, it is difficult to grasp a change in the patient's information since it is a paper tag. In this paper, we propose a system using the electronic triage tag (eTriage) that facilitates emergency medical technicians to grasp patients locations and conditions through visualization. This system provides the following three views of the patients information: (1) Inter-site view which shows on a map an overview of the latest status in multiple first-aid stations including the number of technicians and patients of each triage category; (2) Intra-site view which shows detailed status of each first-aid station including the location, triage category, and vital signs of each patient on a 3D map created based on the environment mapping technique; and (3) Individual view which shows vital information of patients on a tablet PC according to its orientation using the augmented reality technique. In this paper, we describe the design and implementation of the proposed system with some preliminary evaluation results., PerNEM 2012 : Pervasive Networks for Emergency Management , Mar 23, 2012 , Lugano, Switzerland

The goal of this paper is to improve the prediction performance of fault-prone module prediction models (fault-proneness models) by employing over/under sampling methods, which are preprocessing procedures for a fit dataset. The sampling methods are expected to improve prediction performance when the fit dataset is unbalanced, i.e. there exists a large difference between the number of fault-prone modules and not-fault-prone modules. So far, there has been no research reporting the effects of applying sampling methods to fault-proneness models. In this paper, we experimentally evaluated the effects of four sampling methods (random over sampling, synthetic minority over sampling, random under sampling and one-sided selection) applied to four fault-proneness models (linear discriminant analysis, logistic regression analysis, neural network and classification tree) by using two module sets of industry legacy software. All four sampling methods improved the prediction performance of the linear and logistic models, while neural network and classification tree models did not benefit from the sampling methods. The improvements of Fl-values in linear and logistic models were 0.078 at minimum, 0.224 at maximum and 0.121 at the mean., ESEM 2007 : First International Symposium on Empirical Software Engineering and Measurement, 20-21 Sept. 2007, Madrid, Spain

In this paper, we propose a new theory of nonlinear noise reduction with a perfectly musical-noise-free property, where no musical noise is generated even for a high signal-to-noise ratio. To achieve high-quality noise reduction with low musical noise, an iterative spectral subtraction method, i.e., recursively applied weak nonlinear signal processing, has been proposed. Although evaluation experiments indicated the existence of an appropriate parameter setting that gives a musical-noise-free state, no theoretical studies have been carried out. Therefore, in this paper, we theoretically derive pairs of internal parameters that satisfy the musical-noise-free condition by analysis based on higher-order statistics. It is clarified that finding a fixed point in the kurtosis of noise spectra enables the reproduction of the musical-noise-free state, and comparative experiments with commonly used noise reduction methods show the efficacy of the proposed method., ICASSP2012: The 37th International Conference on Acoustics, Speech, and Signal Processing, March 25-30, Kyoto, Japan.

In this paper, we address an improved method of noise reduction used in multichannel Non-Audible Murmur (NAM) based on blind source separation. Recently, speech processing with NAM has been proposed for applying versatile speech interface into quiet environments where we hesitate to utter. NAM is a very soft whispered voice signal detected with the NAM microphone, which is one of the body-conductive microphone. The detected NAM signal always suffers from nonstationary noise caused by speaker's movement because it changes the setting condition of the NAM microphone. In order to reduce the noise signal, blind noise reduction using stereo NAM signals detected with two NAM microphones has been proposed by some of the authors. In this paper, we aim to achieve further improvement in the noise reduction ability by changing the noise estimation and postprocessing algorithms to enhance the target NAM signal. In addition, we evaluate the application of recording the NAM signals with various types of microphones., ISSPIT 2012: The 12th IEEE International Symposium on Signal Processing and Information Technology, December 12-15, 2012, Ho Chi Minh City, Vietnam.

In this paper we present a novel approach to acoustic model training for non-audible murmur (NAM) recognition using normal speech data transformed into NAM data. NAM is extremely soft murmur, that is so quiet that people around the speaker can hardly hear it. It is detected directly through the soft tissue of the head using a special body-conductive microphone, NAM microphone, placed on the neck below the ear. NAM recognition is one of the promising silent speech interfaces for man-machine speech communication. We have previously shown the effectiveness of speaker adaptive training (SAT) based on constrained maximum likelihood linear regression (CMLLR) in NAM acoustic model training. However, since the amount of available NAM data is still small, the effect of SAT is limited. In this paper we propose modified SAT methods capable of using a larger amount of normal speech data by transforming them into NAM data. The experimental results demonstrate that the pro posed methods yield an absolute increase of approximately 2% in word accuracy compared with the conventional method., ICASSP2011: The 36th International Conference on Acoustics, Speech, and Signal Processing, May 22-27, 2011, Prague, Czech Republic.

This paper proposes a novel method for estimating depth without similarity measures such as SSD and NCC. Our idea for estimating a depth map is very simple; only counting interest points in images is integrated with the framework of multi-baseline stereo. Even by a simple algorithm, depth can be determined without computing similarity measures such as SSD and NCC that have been used for conventional stereo matching. The proposed method realizes robust depth estimation against occlusions with lower computational cost. Though a naive TNIP based method can realize fast and robust depth estimation, the accuracy of estimated depth is lower than one by SSSD based method because TNIP uses sparse data. In this paper, we also show that accuracy of depth estimation can be increased by combining TNIP based method and SSSD based method. In experiments, the validity and feasibility of our algorithm are demonstrated for both synthetic and real outdoor scenes., SICE 2007 : International Conference on Instrumentation, Control and Information Technology , Sep 17-20, 2007 , Takamatsu, Japan

In this paper, we provide a new theoretical analysis of the amount of musical noise generated via iterative spectral subtraction based on higher-order statistics. To achieve high-quality noise reduction with low musical noise, the iterative spectral subtraction method, i.e., recursively applied weak nonlinear signal processing, has been proposed. Although the effectiveness of the method has been reported experimentally, there have been no theoretical studies. Therefore, in this paper, we formulate the generation process of musical noise by tracing the change in kurtosis, and conduct a comparison of the amount of musical noise for different parameter settings under the same noise reduction performance. It is clarified from mathematical analysis and evaluation experiments that iterative spectral subtraction with very weak processing can result in the generation of less musical noise., MLSP2010: IEEE International Workshop on Machine Learning for Signal Processing, August 29 - September 1, 2010, Kittila, Finland.

In this paper, we construct a hands-free robot spoken dialogue system based on the real-time blind spatial subtraction array (BSSA) and evaluate the system. BSSA is the blind source extraction method, and the source extraction in BSSA is carried out by subtracting the power spectrum of the estimated noise signal by the independent component analysis from the power spectrum of the target speech partly enhanced signal. Although BSSA can reduce noise signal efficiently, ICA consumes huge amount of computational costs. Thus it is difficult to run BSSA in real-time. In this paper, we newly propose a real-time architecture of BSSA and construct a hands-free robot spoken dialogue system based on the real-time BSSA. In the hands-free robot spoken dialogue system with the real-time BSSA, 6% improvement of the speech recognition result can be seen compared with the conventional speech enhancement methods., IROS2008: IEEE/RSJ International Conference on Intelligent Robotics and Systems , September 22-26, 2008, Nice, France.

In this paper, we propose a new extension framework of multichannel audio coding based on temporal quantization of spatial information. In our previous study, multiple-audio-object signal can be encoded/decoded via prototypes of directional clustering for each audio object. This paper, first, pays attention to the fact that quantized information corresponds to the spatial image of each sound object, and is easily modified and operated by the end users. Next, as a novel application of the spatial image operation, we propose a new earphone leakage noise reduction method in which the mostly leaked sound object can be detected automatically based on the higher-order statistics, and eliminated. The effectiveness of the proposed CODEC is verified in the subjective evaluation, and leakage noise reduction method is assessed in the objective evaluation., ISCCSP2010: 4th International Symposium on Communications, Control and Signal Processing, March 3-5, 2010, Limassol, Cyprus.

In this paper, we derive a closed-form solutuion of the internal parameters that satisfy the musical-noise-free condition by analysis based on higher-order statistics in iterative Wiener filtering. To achieve high-quality noise reduction with low musical noise, musical-noise-free noise reduction method has been proposed, where no musical noise is generated even for a high SNR in iterative spectral subtraction. Howeverm no existence or no mathematical analysis of the musical-noise-free state in iterative Wiener filtering is reported. Therefore, in this paper, we theoretically derive pairs of internal parameters that sarisfy the musical-noise-free condition in iterative Wiener filtering. It is clarified that iterative Wiener filtering method with the optimal parameter settings is adbantageous to the conventional methods in terms of the amounts of musical noise generation and speech distortion., ISSPIT 2012: The 12th IEEE International Symposium on Signal Processing and Information Technology, December 12-15, 2012, Ho Chi Minh City, Vietnam.