Comparing ANN and GMM in a voice conversion framework

In this paper, we present a comparative analysis of artificial neural networks (ANNs) and Gaussian mixture models (GMMs) for design of voice conversion system using line spectral frequencies (LSFs) as feature vectors. Both the ANN and GMM based models are explored to capture nonlinear mapping functions for modifying the vocal tract characteristics of a source speaker according to a desired target speaker. The LSFs are used to represent the vocal tract transfer function of a particular speaker. Mapping of the intonation patterns (pitch contour) is carried out using a codebook based model at segmental level. The energy profile of the signal is modified using a fixed scaling factor defined between the source and target speakers at the segmental level. Two different methods for residual modification such as residual copying and residual selection methods are used to generate the target residual signal. The performance of ANN and GMM based voice conversion (VC) system are conducted using subjective and objective measures. The results indicate that the proposed ANN-based model using LSFs feature set may be used as an alternative to state-of-the-art GMM-based models used to design a voice conversion system.     

Victim retention for reducing cache misses in tiled chip multiprocessors

This paper presents CMP-VR (Chip-Multiprocessor with Victim Retention), an approach to improve cache performance by reducing the number of off-chip memory accesses. The objective of this approach is to retain the chosen victim cache blocks on the chip for the longest possible time. It may be possible that some sets of the CMPs last level cache (LLC) are heavily used, while certain others are not. In CMP-VR, some number of ways from every set are used as reserved storage. It allows a victim block from a heavily used set to be stored into the reserve space of another set. In this way the load of heavily used sets are distributed among the underused sets. This logically increases the associativity of the heavily used sets without increasing the actual associativity and size of the cache. Experimental evaluation using full-system simulation shows that CMP-VR has less off-chip miss-rate as compared to baseline Tiled CMP. Results are presented for different cache sizes and associativity for CMP-VR and baseline configuration. The best improvements obtained are 45.5% and 14% in terms of miss rate and cycles per instruction (CPI) respectively for a 4 MB, 4-way set associative LLC. Reduction in CPI and miss rate together guarantees performance improvement.     

Subspace filtering approach based on orthogonal projection for better analysis of stressed speech under clean and noisy environments

This study explores a novel subspace projection-based approach for analysis of stressed speech. Studies have shown that stress influences the speech production system and it results in a large acoustic variation between the neutral and the stressed speech. This degrades the discrimination capability of an automatic speech recognition system trained on neutral speech when tested on stressed speech. An effort is made to reduce the acoustic mismatch by explicitly normalizing the stress-specific attributes. The stress-specific divergences are normalized by exploiting the subspace filtering technique. To accomplish this, an orthogonal projection based linear relationship between the speech and the stress information has been explored to filter an effective speech subspace, which consists of speech information. Speech subspace is constructed using K-means clustering followed by singular value decomposition method using neutral speech data. The speech and the stress information are separated by projecting the stressed speech orthogonally onto an effective speech subspace. Experimental results indicate that, the bases of an effective subspace comprises the first few eigenvectors corresponding to the highest eigenvalues. To further improve the system performance, both the neutral and the stressed speech are projected onto the lower dimensional subspace. The projections derived using the neutral speech employs heteroscedastic linear discriminant analysis in maximum likelihood linear transformations-based semi-tied adaptation framework. Consistent improvements are noted for the proposed technique in all the discussed cases.     

Long-wave interfacial instabilities in a thin electrolyte film undergoing coupled electrokinetic flows: a nonlinear analysis

Spatiotemporal deformations of the free charged surface of a thin electrolyte film undergoing a coupled electrokinetic flow composed of an electroosmotic flow (EOF) on a charged solid substrate and an electrophoretic flow (EPF) at its free surface are explored through linear stability analysis and the long-wave nonlinear simulations. The nonlinear evolution equation for the deforming surface is derived by considering both the Maxwell’s stresses and the hydrodynamic stresses. The electric potential across the film is obtained from the Poisson–Boltzmann equation under the Debye–Hückel approximation. The simulations show that at the charged electrolyte–air interface, the applied electric field generates an EPF similar to that of a large charged particle. The EOF near the solid–electrolyte interface and the EPF at the electrolyte–air interface are in the same (or opposite) directions when the zeta potentials at the two interfaces are of the opposite (or same) signs. The linear and nonlinear analyses of the evolution equation predict the presence of travelling waves, which is strongly modulated by the applied electric field and the magnitude/sign of the interface zeta potentials. The time and length scales of the unstable modes reduce as the sign of zeta potential at the two interfaces is varied from being opposite to same and also with the increasing applied electric field. The increased destabilization is caused by a reverse EPF near the free surface when the interfaces bear the same sign of zeta potentials. Flow reversal by EPF at the free surface occurs at smaller zeta potential of the free surface when the film is thicker because of less influence of the EOF arising at the solid–electrolyte boundary. The amplitude of the surface waves is found to be smaller when the unstable waves travel at a faster speed. The films can undergo pseudo-dewetting when the free surface is almost stationary under the combined influences of EPF and EOF. The free surface instability of the coupled EOF and EPF has some interesting implications in the development of micro/nano fluidic devices involving a free surface.     

Adaptive broadcast by fault-tolerant spanning tree switching

Adaptation is a desirable requirement in a distributed system as it helps the system to perform efficiently under different environments. For many problems, more than one protocol exists, such that one protocol performs better in one environment while the other performs better in another. In such cases, adaptive distributed systems can be designed by dynamically switching between the protocols as the environment changes. Distributed protocol switching is also important for performance enhancement, or fault-tolerance of a distributed system. In this work, we illustrate distributed protocol switching by providing a distributed algorithm for adaptive broadcast that dynamically switches from a BFS tree to a DFS tree. The proposed switching algorithm can also handle arbitrary crash failures. It ensures that switching eventually terminates in spite of failures and the desired tree (DFS tree) results as the output. We also investigate the properties that can be guaranteed on the delivery of broadcast messages under specific failure conditions. We show that under no failure, each broadcast message is eventually correctly delivered to all the nodes in spite of switching. Under arbitrary crash fault, we ensure that switching eventually terminates with the desired tree as the broadcast topology. We also investigate the specific delivery guarantees that can be provided when a single crash fault happens, both during switching and when no switching is in progress.     

Fairness of Transitions in Diagnosability of Discrete Event Systems

Failure diagnosability has been widely studied for discrete event system (DES) models because of modeling simplicity and computational efficiency due to abstraction. In the literature it is often held that for diagnosability, such models can be used not only for systems that fall naturally in the class of DES but also for the ones traditionally treated as continuous variable dynamic systems. A class of algorithms for failure diagnosability of DES models has been successfully developed for systems where fairness is not a part of the model. These algorithms are based on detecting cycles in the normal and the failure model that look identical. However, there exist systems with all transitions fair where the diagnosability condition that hinges upon this feature renders many failures non-diagnosable although they may actually be diagnosable by transitions out of a cycle. Hence, the diagnosability conditions based on cycle detection need to be modified to hold for many real-world systems where all transitions are fair. In this work, however, it is shown by means of an example that a system may have some transitions fair and some unfair. A new failure diagnosability mechanism is proposed for DES models with both fair and unfair transitions. Time complexity for deciding diagnosability of DES models with fair and unfair transitions is analyzed and compared with the time complexities of other DES diagnosability analysis methods reported in the literature.     

Parameter-uniform hybrid numerical scheme for time-dependent convection-dominated initial-boundary-value problems

In this paper, we propose a numerical scheme which is almost second-order spatial accurate for a one-dimensional singularly perturbed parabolic convection-diffusion problem exhibiting a regular boundary layer. The proposed numerical scheme consists of classical backward-Euler method for the time discretization and a hybrid finite difference scheme for the spatial discretization. We analyze the scheme on a piecewise-uniform Shishkin mesh for the spatial discretization to establish uniform convergence with respect to the perturbation parameter. Numerical results are presented to validate the theoretical results.     

Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease

In this paper, we have made medical application of a new artificial immune system named the information gain based artificial immune recognition system (IG-AIRS) which minimizes the negative effects of taking into account all attributes in calculating Euclidean distance in shape–space representation which is used in many artificial immune systems. For medical data, thyroid disease data set was applied in the performance analysis of our proposed system. Our proposed system reached 95.90% classification accuracy with 10-fold CV method. This result ensured that IG-AIRS would be helpful in diagnosing thyroid function based on laboratory tests, and would open the way to various ill diagnoses support by using the recent clinical examination data, and we are actually in progress.     

Work-related pains among the workers associated with pineapple peeling in small fruit processing units of North East India

The present study aimed at investigating work-related pain issues among the workers associated with pineapple peeling activity in small fruit processing units of North East India. The study also assessed the associated levels of ergonomic risk prevalent among the workers and identified ergonomic risk factors associated with pineapple peeling task. A cross-sectional survey was conducted using questionnaire-based interview, pain self-report and direct observation of the activities to understand the prevailing work conditions. Rapid Upper Limb Assessment (RULA) was used to assess the level of ergonomic risks involved. Prevalence of pain, in different body parts of the participants, was found evident with a higher percentage of participants reporting pain in shoulders (41.1%), upper arms (37.1%) and lower back (45.7%) while some had also reported of pain in neck (13.2%), lower arms (15.9%), wrists (12.6%) and palm (6%). For RULA, 89.4% of the participants had a grand score greater than equal to 5 which fell under action level 3 indicating for further investigation and changes soon. Hence, various risk factors influencing pain occurrences were identified using logistic regression, and factors like age, gender, hours of peeling, frequency of rest breaks, perceived work fatigue and years of experience, were found to be associated with risk of pain in at least one of the body parts. Identification of these risk factors laid down path for the future course of actions and improvements to address the work pain related issues among workers.Relevance to industryThe study established pain prevalence associated with manual pineapple peeling task and identified the potential risk factors, laying down basis for possible future actions to address work pain related issues and enhance productivity of the workers.     

A distributed data clustering algorithm in P2P networks

Clustering is one of the important data mining issues, especially for large and distributed data analysis. Distributed computing environments such as Peer-to-Peer (P2P) networks involve separated/scattered data sources, distributed among the peers. According to unpredictable growth and dynamic nature of P2P networks, data of peers are constantly changing. Due to the high volume of computing and communications and privacy concerns, processing of these types of data should be applied in a distributed way and without central management. Today, most applications of P2P systems focus on unstructured P2P systems. In unstructured P2P networks, spreading gossip is a simple and efficient method of communication, which can adapt to dynamic conditions in these networks. Recently, some algorithms with different pros and cons have been proposed for data clustering in P2P networks. In this paper, by combining a novel method for extracting the representative data, a gossip-based protocol and a new centralized clustering method, a Gossip Based Distributed Clustering algorithm for P2P networks called GBDC-P2P is proposed. The GBDC-P2P algorithm is suitable for data clustering in unstructured P2P networks and it adapts to the dynamic conditions of these networks. In the GBDC-P2P algorithm, peers perform data clustering operation with a distributed approach only through communications with their neighbours. The GBDC-P2P does not need to rely on a central server and it performs asynchronously. Evaluation results demonstrate the superior performance of the GBDC-P2P algorithm. Also, a comparative analysis with other well-established methods illustrates the efficiency of the proposed method.