A dynamic latent variable model for source separation

We propose a novel latent variable model for learning latent bases for time-varying non-negative data. Our model uses a mixture multinomial as the likelihood function and proposes a Dirichlet distribution with dynamic parameters as a prior, which we call the dynamic Dirichlet prior. An expectation maximization (EM) algorithm is developed for estimating the parameters of the proposed model. Furthermore, we connect our proposed dynamic Dirichlet latent variable model (dynamic DLVM) to the two popular latent basis learning methods - probabilistic latent component analysis (PLCA) and non-negative matrix factorization (NMF). We show that (i) PLCA is a special case of the dynamic DLVM, and (ii) dynamic DLVM can be interpreted as a dynamic version of NMF. The effectiveness of the proposed model is demonstrated through extensive experiments on speaker source separation, and speech-noise separation. In both cases, our method performs better than relevant and competitive baselines. For speaker separation, dynamic DLVM shows 1.38 dB improvement in terms of source to interference ratio, and 1 dB improvement in source to artifact ratio

Socio-Economic Conditions and Quality of Life in the Tribal Areas of Orissa with Special Reference to Mayurbhanj District

Odisha (previously known as Orissa), being socio-economically backward but culturally sound, is one of the important states in Eastern India. Out of 30 districts 9 are considered as tribal districts (according to Location Quotient value) and of the total population (41,947,358 in 2011) a significant share (22.1%) goes to tribal people (8,145,081in 2011). This tribal group of Odisha has special significance because they are one of the most backward and geographically isolated communities. That’s why their life style and economy is confined to the direct utilization of natural resources, pre-agricultural level of technology and specific indigenous type of work. But now with the emergence of industry and market economy, the age-old relationship between tribes and nature has disturbed. Keeping this in backdrop, the present study tried to explore the changing scenario of socio-economic condition in the tribal areas of Odisha. In this regard, various socio-economic indicators have been analyzed and compared for representing district-level patterns of quality of life and finding out the variation among the Primitive tribal households in the study area. In addition, Mayurbhanj has also been taken as a case study to represent the socio-economic condition and quality of life at the block level. It may be pointed out in this context that out of 30 districts in Odisha, according to Location Quotient value Mayurbhanj is the highest tribal concentrated district. The overall objective of this study is to obtain a better understanding of disparities and variations in socio-economic status in Odisha as well as in Mayurbhanj and also find out some remedial measures to overcome the problems to bring the Primitive tribal community in the main stream of the society. Maps have been prepared on the above-mentioned indicators based on secondary data using Arc-GIS 9.3. From the analysis of the health-related indicators it is clear from the analysis that the quality of life in the district has improved remarkably over the years but socio-economic disparities in terms of caste and gender continue to be a major problem mostly in tribal and backward areas

Dirichlet latent variable model : a dynamic model based on Dirichlet prior for audio processing

We propose a dynamic latent variable model for learning latent bases from time varying, non-negative data. We take a probabilistic approach to modeling the temporal dependence in data by introducing a dynamic Dirichlet prior – a Dirichlet distribution with dynamic parameters. This new distribution allows us to assure non-negativity and avoid intractability when sequential updates are performed (otherwise encountered in using Dirichlet prior). We refer to the proposed model as the Dirichlet latent variable model (DLVM). We develop an expectation maximization algorithm for the proposed model, and also derive a maximum a posteriori estimate of the parameters. Furthermore, we connect the proposed DLVM to two popular latent basis learning methods - probabilistic latent component analysis (PLCA) and non-negative matrix factorization (NMF).We show that (i) PLCA is a special case of our DLVM, and (ii) DLVM can be interpreted as a dynamic version of NMF. The usefulness of DLVM is demonstrated for three audio processing applications - speaker source separation, denoising, and bandwidth expansion. To this end, a new algorithm for source separation is also proposed. Through extensive experiments on benchmark databases, we show that the proposed model out performs several relevant existing methods in all three applications

IN VITRO ANTI OXIDANT ACTIVITY OF CHROMATOGRAPHICALLY SEPARATED FRACTIONS FROM THE LEAVES OF AGERATUM CONYZOIDES L

Synthetic anti oxidants are not safe for human health. It is often claimed that they may develop carcinoma in human body. Therefore, search for natural anti oxidants was going on and extended up to plant sources. Many medicinal plants are known having anti oxidant activity. Aageratum conyzoides Linn. is one such plant. In order to isolate anti oxidant compound (s) from the leaves of A. conyzoides L. the present study was undertaken. In isolation study silica gel G column chromatography of the powdered leaves of A.conyzoides L. was done when six fractions were separated. In vitro anti oxidant activity of these six fractions was measured by superoxide anion generation with help of xanthine-xanthine oxidase assay and with linoleic acid peroxidation assay as well as DPPH photometric assay. Results showed that fourth fraction had maximum anti oxidant activity. Inhibitory activities of xanthine oxidation, linoleic acid peroxidation and scavenging capacity of DPPH by the fourth fraction were respectively 96%, 97% and 96% whereas for other five fractions inhibitory activities were quite low. Anti oxidant activity is known to be associated with compounds like total phenol, flavonoids, ascorbic acid and carotenoids. These compounds were estimated in the separated six fractions after chromatography of powdered leaves of A. conyzoides L. Results showed that fourth fraction had total phenol, flavonoids, ascorbic acid and carotenoids in the concentrations of 58 mg/mg dry wt, 88 mg/mg dry wt, 22 mg/g dry wt and 25 mg/g dry wt respectively. The amounts were significantly higher in comparison to that of other fractions. In vitro anti oxidant activity of the fourth fraction was, therefore, related with high amounts of total phenol, flavonoids, ascorbic acid and carotenoids. Present study indicated that the separated fourth fraction after silica gel G column chromatography of powdered leaves of A.conyzoides L. may be used as natural anti oxidant

Impact of Lean and Sustainability Oriented Innovation on Sustainability Performance of Small and Medium Sized Enterprises: A Data Envelopment Analysis-based framework

Lean and Sustainability Oriented Innovation both enhance competitiveness of small and medium enterprises (SMEs) in a sustainable way. Lean is efficiency focused, whereas Sustainability Oriented Innovation emphasizes on responsiveness. Although lean and sustainability oriented innovation have been separately researched, there is a gap in knowledge on the combined effect of lean and sustainability oriented innovation (SOI) on SMEs Supply Chain sustainability. SMEs have limited resources and face numerous competition. Therefore, their supply chain sustainability can only be achieved through most appropriate trade-off between economic, environment and social aspects of business. The purpose of this paper is to understand the combined effect of sustainability oriented innovation and lean practices, on supply chain sustainability performance of SMEs. The study uses a Data Envelopment Analysis (DEA) based framework and applies this to a group of SMEs within the Eastern part of India. Lean and sustainability oriented innovation are considered as input criteria, and economic, operational, environmental and social aspects are considered as output criteria of the proposed framework. DEA segregates inefficient SMEs and suggests at least a SME to benchmark. Subsequently, the study undertakes qualitative approach to suggest improvement measures for the inefficient SMEs. The results reveal that combined lean and SOI helps achieve SMEs' supply chain sustainability. The findings are useful for policy makers and Individual SMEs' owners and managers to undertake measures for improving sustainability. Theoretically this research contributes a DEA-based framework to study the effect of combined lean and SOI on sustainability that helps improving SMEs’ sustainability performance

Fault detection in a centrifugal pump using vibration and motor current signature analysis

Due to growth of mechanisation and automation, today’s industrial systems are becoming more complex. A small breakdown of any non-redundant machine component affects the operation of the entire system. To increase the availability and reliability, automated health monitoring and self-diagnostic capability (SDC) becoming essential to many industrial machineries like pumps, motors, etc. Condition monitoring does not prevent the failure, but it can predict the possibility of future failure by measuring certain machine parameters. Though there are various condition monitoring techniques, vibration analysis and motor current signature analysis (MCSA) are most suitable for detection of faults and abnormalities in machine systems. This work attempts to develop an SDC framework and diagnose the impeller condition of a centrifugal pump using MCSA. Time and frequency domain analyses are done for different impeller conditions of the pump, such as normal impeller and defective impellers. Significant differences are observed and a fault prediction strategy is recommended.Peer reviewe