A cryptic encoding method for G.729 using variation in bit‐reversal sensitivity

ABSTRACTThis paper describes a method of extracting time-varyingfeatures that is effective for speech signals with high funda-mental frequencies. The proposed method adopts a speechproduction model that consists of a Time-Varying Auto-Regressive (TVAR) process for an articulatory filter and aHidden Markov Model (HMM) for an excitation source.The model represents waveform amplitude variations bytime-varying gain of the excitation source. The proposed algo-rithm is given by extending a Viterbi algorithm so that theproposed algorithm can adaptively estimate TVAR coeffi-cients and time-varying gain with decoding the state tran-sition of the excitation source HMM. We applied the pro-posed method to extracting time-varying features from bothsynthetic and natural speech, and confirmed its feasibility.1. INTRODUCTIONThe conventional Linear Prediction (LP) method is widelyused to analyze speech signals[1]. However, several prob-lems still remain to be solved[2]. One such problem is thatlocal peaks of the LP spectral estimate are strongly biasedtoward the harmonics, especially for high-pitched speech.Several methods have been designed to overcome this prob-lem [3, 4, 5, 6]. The authors have previously indicated thatan analysis method based on a speech production modelconsisting of an Auto-Regressive (AR) process for an artic-ulatoryfilterand a HiddenMarkov Model(HMM)for an ex-citationsource is robustforhighfundamental frequencies[7,8]. However, this method is not suitable for analyzing con-tinuous speech for the following reasons. First, the AR co-efficients and HMM are iteratively estimated within everyanalysis frame, so a large number of operations is needed.Second, the analysis frame size needs to be large in orderto guarantee stable learning of the excitation source HMM.Third, the model parameters are assumed to be constantwithin the analysis frame, so the resulting parameters areaveraged within such a long analysis frame. This makesit difficult to extract the dynamic characteristics of speechwhen features change rapidly, like in a singing voice.In this paper, we extend the speech production model in[7] so that the proposed model can represent time-varyingfeatures of continuous speech. We also describe an anal-ysis method that adaptively estimates Time-Varying Auto-Regressive (TVAR) coefficients and gain based on the newmodel. The proposed method can substantially reduce thenumber of operations by applying the learned HMM andcan also extract dynamic characteristics of continuous speechby estimating those time-varying features adaptively.2. SPEECH PRODUCTION MODEL BASED ONTVAR-HMMThe proposed method adopts a speech production modelthat consists of a TVAR process for an articulatory filterand an HMM for an excitation source. The nodes of theHMM are concatenated in a ring state in order to representperiodicity of voiced sounds. We have previously shownthat LP analysis incorporating the excitation source HMMcan precisely estimate the characteristics of both vocal tractand excitation source from high-pitched speech signal[7, 8].The proposed model represents the time-varying features ofnot only the vocal tract but also the waveform amplitude bymultiplying an excitation source emitted from the HMM bya time-varying gain.

Reactive power compensation is an important challenge in smart power systems. However, in the context of reactive power compensation, most existing studies assume that customers can assess their compensation value (their Var unit) objectively. In this paper, customers are assumed to make decisions that pertain to reactive power coordination. In consequence, the way in which those customers evaluate the compensation value resulting from their individual decisions will impact the overall grid performance. In particular, a behavioral framework, based on the framing effect of prospect theory (PT), is developed to study the impact of both objective value and subjective evaluation in a reactive power compensation game. For instance, PT's framing effect allows customers to optimize a subjective value of their utility which essentially frames the objective utility with respect to a reference point. This game enables customers to coordinate the use of their electrical devices to compensate reactive power. For the proposed game, both the objective case using expected utility theory and the PT consideration are solved via a learning algorithm that converges to a mixed-strategy Nash equilibrium. In addition, several key properties of this game are derived analytically. Simulation results show that, under PT, customers are likely to make decisions that differ from those predicted by classical models. For instance, using an illustrative two-customer case, we show that a PT customer will increase the adoption of a conservative strategy (achieving a high power factor) by 29% compared to a conventional customer. Similar insights are also observed for a case with three customers.

We present SATYA, a system that computes a reputation value for Web service providers in order to enhance the service discovery and selection process increasing reliability in SOA transactions. In this work, objective values of service evaluations supplied by monitoring entities are used along with subjective evaluations supplied by service consumers. The objective and subjective values are compared in order to: (i) validate subjective evaluations; (ii) minimize the degree of subjectivity of computed reputation values; and (iii) discover consumers' preferences in terms of QoS metrics. By assigning Web services a trustable reputation value, SATYA enhances the service descriptions provided by registries with additional information to be used during the service discovery phase.

PurposeTo systematically characterize and objectively evaluate basic railway safety management capability, creating a closed-loop management approach which allows continuous improvement and optimization.Design/methodology/approachA basic railway safety management capability evaluation index system based on a comprehensive analysis of national safety management standards, railway safety rules and regulations and existing safety data from railway transport enterprises is presented. The system comprises a guideline layer including safety committee formation, work safety responsibility, safety management organization and safety rules and regulations as its components, along with an index layer consisting of 12 quantifiable indexes. Game theory combination weighting is utilized to integrate subjective and objective weight values derived using AHP and CRITIC methods and further combined using the TOPSIS method in order to construct a comprehensive basic railway safety management capability evaluation model.FindingsThe case study presented demonstrates that this evaluation index system and comprehensive evaluation model are capable of effectively characterizing and evaluating basic railway safety management capability and providing directional guidance for its sustained improvement.Originality/valueConstruction of an evaluation index system that is quantifiable, generalizable and accessible, accurately reflects the main aspects of railway transportation enterprises’ basic safety management capability and provides interoperability across various railway transportation enterprises. The application of the game theoretic combination weighting method to derive composite weights which combine experts’ subjective evaluations with the objectivity of data.

Driver's feeling is variously affected by lots of components such as engine, frame, wheels, and seats during the operation of automobiles. The main objective of this research is to identify the correlation between subjective evaluation and vibration metrics that was set by ISO to investigate development of the car vibration quality index using multiple linear regressions(MLR). A previous research related with automotive vibration quality used the method of calculating acceleration values of the point of a seat, a seat back, foot as RMS for objective evaluation. The automotive comfort is determined by RMS values. In comparison with the previous research, this study includes not only the vibration metrics, but also subjective values by jury evaluation. By indentifying the correlation between subjective evaluation and vibration metrics, the automotive vibration quality index is developed through MLR. Based on the results of this study, the proposed the automotive vibration quality index which developed through MLR will be helpful to obtain objective and reliable automotive comfort values.

Lengthy evaluation times are common in many optimization problems such as direct policy search tasks, especially when they involve conducting evaluations in the physical world, for example, in robotics applications. Often when evaluating solution over a fixed time period, it becomes clear that the objective value will not increase with additional computation time (e.g., when a two-wheeled robot continuously spins on the spot). In such cases, it makes sense to stop the evaluation early to save computation time. However, most approaches to stop the evaluation are problem specific and need to be specifically designed for the task at hand. Therefore, we propose an early stopping method for direct policy search. The proposed method only looks at the objective value at each timestep and requires no problem-specific knowledge. We test the introduced stopping criterion in five direct policy search environments drawn from games, robotics, and classic control domains and show that it can save up to \(75\%\) of the computation time. We also compare it with problem-specific stopping criteria and show that it performs comparably, while being more generally applicable.

A method for quickly bounding the optimal objective value of an OPF problem using a semidefinite relaxation and a local solution

Access control is an important means of ensuring information security, and trust-based access control has been considered as an effective mechanism for access control in open networks. To make access control mechanisms more effective, the assessment of trust has become a key issue. Among the different components of trust, recommendation trust plays a key role in environments and applications in which there are not enough direct interactions between entities, making direct trust less reliable. In this paper, we propose a method for the assessment of recommendation trust in which we try to solve the problem of weight assignment to decision attributes by using relevant theories in expert investigation, fuzzy analysis and gray correlation analysis. We also perform some experiment to show that the results of the proposed assessment method are in line with the actual ones to demonstrate the effectiveness of the proposed method as well as its advantages over some other comparable methods.

Color images are widely used to disseminate information via websites and smartphone applications. Red-green deficients may have difficulty distinguishing the colors, and this would cause ineffective visual communication. Recently, a naturalness preserving image recoloring method for red-green deficients was introduced. However, the colors of objects in the recolored images may still look similar to them. Thus, this paper proposes a flexible color contrast enhancement method for red-green deficiency. The proposed method introduces a contrast parameter to exaggerate the blue stimulation of the recolored images. Moreover, the proposed method provides a flexible way for red-green deficients to view the image based on their preference. Objective and subjective evaluations were conducted to evaluate the performance of the proposed method. Results from subjective evaluation performed by the red-green deficients showed that the proposed method obtained better preference scores in terms of color contrast enhancement and their overall preference.

Time-Scale Modification (TSM) is a well-researched field and allows for time-domain manipulation of a signal without modifying the pitch or timbre. Many TSM methods have been presented, however quantitative results on the quality of these methods are rare, with most methods reporting informal listening tests. This is likely due to the timecommitment and cost of subjective testing. Additionally, an objective measure of quality has not yet been developed that is suitable for timescaled signals. This dissertation describes the design of e ective objective measures of quality for TSM. TSM methods are, generally, single channel algorithms that give poor results when applied to multi-channel signals, as the phase relationship between channels must be maintained. This dissertation proposes a method and additional variant for maintaining the phase relationship between channels and retaining the presence in the centre of the stereo signal. The method involves pre- and post-processing the signal, with the variant processing each frame for real-time suitability. Sum and di erence transformations of the stereo signal are used for TSM and result in a large improvement in stereo phase coherence, consequently maintaining the stereo field. The proposed method produces a highquality stereo output and greatly improves quality over the independent channel processing method. It also allows for simple implementation around all existing TSM frameworks. A modification to the Epoch-Synchronous Overlap-Add (ESOLA) TSM algorithm is proposed in this dissertation. The proposed method, Fuzzy Epoch-Synchronous Overlap-Add, improves on the previous ESOLA method through cross-correlation of time-smeared epochs before overlap-adding. This reduces distortion and artefacts while the speaker's fundamental frequency is stable, as well as reducing artefacts during pitch modulation. The proposed method is tested against well-known TSM algorithms. It is preferred over ESOLA and gives similar performance to other TSM algorithms for voice signals. It is also shown that this algorithm can work effectively with solo instrument signals containing strong fundamental frequencies. No effective objective measure of quality for TSM exists. This dissertation details the creation, subjective evaluation and analysis of a dataset, for use in the development of an objective measure of quality for TSM. Comprising two parts, the training subset contains 88 source files processed using six TSM methods at 10 time-scales, while the testing subset contains 20 source files processed using three additional methods at four time-scales. The source material contains speech, solo harmonic and percussive instruments, sound effects and a range of music genres. 42,529 ratings were collected from 633 sessions using laboratory and remote collection methods. Analysis of results shows no correlation between age and quality of rating; equivalence between expert and non-expert listeners; negligible di erences between participants with and without hearing issues; and negligible di erences between testing modalities. Comparison of published objective measures and subjective scores shows the objective measures to be poor indicators of subjective quality. Initial results for a retrained objective measure of quality are presented with results approaching average loss and correlation values of subjective sessions. An objective measure of quality for time-scaled audio is proposed that makes use of the previously developed dataset and improves on reported results. The measure uses hand-crafted features and a fully connected network to predict subjective mean opinion scores. Basic and Advanced Perceptual Evaluation of Audio Quality features are used in addition to nine features specific to TSM artefacts. Six methods of alignment are explored, with interpolation of the reference magnitude spectrum to the length of the test magnitude spectrum giving the best performance. The proposed measure achieves an average Root Mean Squared Error (RMSE) of 0.490 and a mean Pearson Correlation Coe cient (PCC) of 0.864, equivalent to 97th and 82nd percentiles of subjective sessions respectively. The proposed measure is used to evaluate TSM algorithms, finding that Elastique gives the highest objective quality for solo instrument and voice signals, while the Identity Phase-Locking Phase Vocoder gives the highest objective quality for music signals and the best overall quality. Two single-ended objective quality measures for time-scaled audio are also proposed. These measure do not require a reference signal, nor alignment. Data driven features are created by either a convolutional neural network (CNN) or a bidirectional gated recurrent unit (BGRU) network, and are fed to a fully-connected network to predict subjective mean opinion scores. The proposed CNN and BGRU measures achieve an average RMSE of 0.608 and 0.576, and a mean PCC of 0.771 and 0.794, respectively. The proposed measures are used to evaluate TSM algorithms, and comparisons are provided for 16 TSM implementations. A literature review is included with required background knowledge. It includes the fundamentals of sound perception, sound capture, digital signal processing, time-scale modification methods used within research, and subjective and objective measures of quality. Full implementation of all proposed methods and measures can be found at github.com/zygurt/TSM, while the labelled dataset is available at http://ieee-dataport.org/1987.

This paper presents a novel effective methodology for sound quality analysis and prediction of vehicle interior noise using gray system theory. Four objective psychoacoustic parameters selected to evaluate acoustic performance of vehicle interior environment are loudness, sharpness, roughness, and fluctuation. Subjective evaluation is presented using paired comparison method, and Bradley–Terry model is also created. The relationship between subjective evaluation and psychoacoustic parameters is determined by using gray relational analysis. Meanwhile, the correlation among psychoacoustic parameters is also revealed. Sound quality prediction models are created based on GM (0, N) and GM (1, N) model. The prediction results show that GM (1, N) model is more capable than GM (0, N) model for prediction of sound quality. Thus, the analysis and prediction results confirm that the proposed method in this study can be a useful tool to analyze and predict sound quality of the vehicle interior noise.

A surrogate-assisted evolution strategy for constrained multi-objective optimization

SummaryLast‐mile bottleneck, fire‐wall bottleneck, and internal network congestion bottleneck are well‐known performance bottleneck problems of private sector in national R&E network since Internet is adopted for R&E network. The emergency of big data science makes these problems more severe because a famous scientist under such a circumstance is severely restricted in his/her big data R&D. Therefore, government cannot leave these stubs in the realm of a private organization any longer. We, KREONET (Korea Research Environment Open Network), tried to solve these problems by SDN‐IP (software‐defined network—Internet protocol) based on ONOS (Open Network Operation System); SDN‐IP is the leading technology for the softwarization of network, and it is developed by On Lab. KREONET began to deploy SDN‐IP as a tool for the softwarization of Korea R&E network even though others studied to business its' feature for the enhancement of resource management of data center first. The initial goal during the construction of SDN‐IP for KREONET is to solve well‐known problems in R&E network, which are related with last‐mile, fire‐wall, and internal network congestion. These 3 problems are not solved for a long time because it costs too much when we try to solve them by the way of the existed hardware networking. In this paper, we introduce our experience that can catch two rabbits at the same time. These experiences are about the provision of the initial popular service of SDN‐IP with the solution of those well‐known bottleneck problems and about the economic way of SDN‐IP construction by incremental hybrid networking of legacy Internet and SDN‐IP.

In this work a method to detect stator insulation degradation of traction drives based on analyses of the transient current reaction caused by inverter switching is proposed. Two simple excitation methods are proposed to analyze the state of the inter-turn insulation as well as the groundwall insulation. With the standard hall-effect based closed loop current transducers used for the control of the machine as well, the transient signal ringing resulting from a switching transition of the inverter is analyzed. Based on a comparative monitoring method the transient part of the current response is analyzed to extract an insulation state indicator of the winding system. The approach is tested with a SiC-MOSFET inverter prototype as a source of excitation. Due to fast switching of the voltage source inverter and high output voltage dv/dt rates, the motor windings suffer under increased stress that leads to insulation degradation. With the ability to change the effective gate resistance small dv/dt variations and their influence on the proposed method is analyzed. The method is tested with 1.4MW and 5.5kW induction machines.

All-in-focus imaging using average filter-based relative focus measure