Deep Neural Networks for Speech Enhancement in Complex-Noisy Environments

In this paper, we considered the problem of the speech enhancement similar to the real-world environments where several complex noise sources simultaneously degrade the quality and intelligibility of a target speech. The existing literature on the speech enhancement principally focuses on the presence of one noise source in mixture signals. However, in real-world situations, we generally face and attempt to improve the quality and intelligibility of speech where various complex stationary and nonstationary noise sources are simultaneously mixed with the target speech. Here, we have used deep learning for speech enhancement in complex-noisy environments and used ideal binary mask (IBM) as a binary classification function by using deep neural networks (DNNs). IBM is used as a target function during training and the trained DNNs are used to estimate IBM during enhancement stage. The estimated target function is then applied to the complex-noisy mixtures to obtain the target speech. The mean square error (MSE) is used as an objective cost function at various epochs. The experimental results at different input signal-to-noise ratio (SNR) showed that DNN-based complex-noisy speech enhancement outperformed the competing methods in terms of speech quality by using perceptual evaluation of speech quality (PESQ), segmental signal-to-noise ratio (SNRSeg), log-likelihood ratio (LLR), weighted spectral slope (WSS). Moreover, short-time objective intelligibility (STOI) reinforced the better speech intelligibility

Six Sigma in Synergy with Risk Management

Because of globalization, stiff competition, Rapid market change?higher environmental uncertainty and lower technology cycle time, it is inevitable to include risk management in the six sigma methodology no matter whether the organization is manufacturing concern or service concern. Risk management is to play a basic role in Define, Measure, Analyze, Improve and Control phase (DMAIC) and Define, Measure, Analyze, Design and Verify  phase (DMADV) in the supply chain. In this paper a need is established using exiting literature to include risk management into six sigma methodology and its potential benefits are described. Keywords: Six Sigma, Risk Management, Supply Chain Managemen

Diet Composition and Seasonal Fluctuations in the Feeding Habit of Snow Barbel (Schizothorax plagiostomus) in River Indus, Pakistan

Background:Schizothorax plagiostomus is widely distributed in river Indus and is most important food fish in Pakistan. The feeding habit of fish is directly related to the size of fish, its metabolic rate and environmental temperature. The accurate description of fish diet and feeding habit is a very important aspect in fisheries management for the purpose of species conservation, breeding and culture. The present work was aimed to investigate the specie abundance, the diet composition and seasonal variations in the feeding habit of Snow barbell Schizothorax plagiostomus.Materials, Methods & Results: A total of 1799 fish specimens were caught at the confluence of six tributaries along river Indus at Indus Kohistan, northeastern Pakistan. The fish were collected by 5-panels of gill net during first week of each month. The site specific Catch Per Unit Effort (CPUE) and season specific CPUE of fish fauna were assessed. For the gut content analysis 240 samples (99 male and 141 females) of S. plagiostomus were selected on monthly basis. Frequency of occurrence method and volumetric method were applied to record the different food items in the gut of S. plagiostomus. The physico-chemical parameters, NO3 concentration and dissolved Co2 of water from different localities of river Indus were recorded month wise by Hach sensION 156 meter, Horiba LAQUA Nitrate Meter and EA80 meter respectively. Significant difference was observed in water temperature during the four seasons. Except alkalinity no other water parameter showed significant variation across different localities. The results showed that highest Mean CPUE was observed for Darel Stream (0.55) and lowest for Jalkot stream (0.26). Peak abundance of fish was recorded in the month of November with a mean catch of 44.50, mean CPUE of 0.74 and mean Kruskal-Wallis rank value of 63.25. Spirogyra and Ulothrix occurred as maximum food items in the gut of S. plagiostomus during summer while their minimum amount occurred during autumn. According to the ranking index spirogyra and ulothrix ranked higher with significant difference in comparison to other food items. The results showed that S. plagiostomus is phytophagous in its feeding habit, which consumed mainly algae attached to stones and pebbles during the whole year. However, the presence of some secondary items such as animal matter, detritus, sand and mud might be due to the distinct availabilities of food along the seasons. The highest feeding activity of S. plagiostomus was recorded during summer while the lowest one occurred during autumn, spring and winter. Discussion: Catch per unit effort (CPUE) is an indirect measure of the abundance of a target species. It is used as an index of stock abundance in fisheries and conservation biology. During the study low fish fauna was found in River Indus as reported previously. Majority of the fish occurred in snow fed river tributaries in the study area as these tributaries are comparatively less turbulent. Previous studies have also recorded that Schizothoracine generally prefer clean waters. The present findings of gut contents analysis showed clearly that S. plagiostomus is a phytophagous fish which scrap and consumed spirogyra and ulothrix attached to stones and pebbles. Earlier it was reported that mouth of S. plagiostomusis is inferior, wide, with deep lower jaw having keratinized cutting edge and the lower lip is folded and expanded with numerous papillae making it best suited for scrapping algae attached to stones and pebbles. The highest feeding activity was observed during warmer months as compared to cold months. S. plagiostomus spawn twice in a year in autumn and in spring. The highest feeding activity of S. plagiostomus seems to be link with a reflex of recovery strategy due to physiological process of gonadal development

Triboelectric Nanogenerator Based Self-Powered Tilt Sensor

This work focuses on the fabrication and evaluation of triboelectric nanogenerator (TENG) based self-powered tilt sensor. The proposed fabricated structure is composed of polydimethylsiloxane (PDMS), steel ball, gold (Au) as electrode and circular ring housing. A specific configuration of electrodes was used to measure the tilt at different angles. FEM simulations were used to verify the electric potential at the electrodes at different angles. The outputs of the fabricated sensor were measured at different angles from 0 to 360°. A sensitivity of 254 mV/rad is obtained in single axis. The TENG based tilt sensor generates an open circuit voltage of 450 mV at 10 MΩ

Radiation pattern synthesis in conformal antenna arrays using modified convex optimization technique

In this paper, a modified convex optimization technique is used for radiationpattern correction in a cylindrical-shaped conformal microstrip array antenna.The technique uses numerical simulations to optimize the amplitude andphase excitations, with the goal to decrease the Euclidean distance betweenthe desired field pattern and the obtained (simulated/measured) field patternwhile maintaining the main beam direction, null's location, and side lobelevels under control. Two prototypes of 1 4 and 2 4 conformal microstripantenna array deformed from linear/planar structure to the prescribed cylin-drical shape, with different radii of curvature, are studied to demonstrate theperformance of the proposed technique. The proposed convex optimizationmodel when applied to conformal antenna array possesses fast computingspeed and high convergence accuracy for radiation pattern synthesis, whichcan be a valuable tool for engineering applications.Dr. Mohammad Alibakhshikenari acknowledges supportfrom the CONEX-Plus programme funded by Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation programme under theMarie Sklodowska-Curie grant agreement No. 801538

ERBM-SE: Extended Restricted Boltzmann Machine for Multi-Objective Single-Channel Speech Enhancement

Machine learning-based supervised single-channel speech enhancement has achieved considerable research interest over conventional approaches. In this paper, an extended Restricted Boltzmann Machine (RBM) is proposed for the spectral masking-based noisy speech enhancement. In conventional RBM, the acoustic features for the speech enhancement task are layerwise extracted and the feature compression may result in loss of vital information during the network training. In order to exploit the important information in the raw data, an extended RBM is proposed for the acoustic feature representation and speech enhancement. In the proposed RBM, the acoustic features are progressively extracted by multiple-stacked RBMs during the pre-training phase. The hidden acoustic features from the previous RBM are combined with the raw input data that serve as the new inputs to the present RBM. By adding the raw data to RBMs, the layer-wise features related to the raw data are progressively extracted, that is helpful to mine valuable information in the raw data. The results using the TIMIT database showed that the proposed method successfully attenuated the noise and gained improvements in the speech quality and intelligibility. The STOI, PESQ and SDR are improved by 16.86%, 25.01% and 3.84dB over the unprocessed noisy speech

Automated Detection of COVID-19 using Chest X-Ray Images and CT Scans through Multilayer- Spatial Convolutional Neural Networks

The novel coronavirus-2019 (Covid-19), a contagious disease became a pandemic and has caused overwhelming effects on the human lives and world economy. The detection of the contagious disease is vital to avert further spread and to promptly treat the infected people. The need of automated scientific assisting diagnostic methods to identify Covid-19 in the infected people has increased since less accurate automated diagnostic methods are available. Recent studies based on the radiology imaging suggested that the imaging patterns on X-ray images and Computed Tomography (CT) scans contain leading information about Covid-19 and is considered as a potential automated diagnosis method. Machine learning and deep learning techniques combined with radiology imaging can be helpful for accurate detection of the disease. A deep learning approach based on the multilayer-Spatial Convolutional Neural Network for automatic detection of Covid-19 using chest X-ray images and CT scans is proposed in this paper. The proposed model, named as the Multilayer Spatial Covid Convolutional Neural Network (MSCovCNN), provides an automated accurate diagnostics for Covid-19 detection. The proposed model showed 93.63% detection accuracy and 97.88% AUC (Area Under Curve) for chest x-ray images and 91.44% detection accuracy and 95.92% AUC for chest CT scans, respectively. We have used 5-tiered 2D-CNN frameworks followed by the Artificial Neural Network (ANN) and softmax classifier. In the CNN each convolution layer is followed by an activation function and a Maxpooling layer. The proposed model can be used to assist the radiologists in detecting the Covid-19 and confirming their initial screening

Hexagonal Printed Monopole Antenna with Triple Stop Bands for UWB Application

Inherently UWB (Ultra Wideband) communication systems comes with interference problem with some if the existing narrowband communication systems. These bands are stopped with the help of band-stop filter in order to reduce electromagnetic interference However, the complexity and limitations are increased due to these filters, hence this solution is turned down in those applications where design complications and complexity is of concern. Introducing various slots of specific shapes and exact dimensions however, have solved this issue for the researchers around the world. This paper presents a hexagonal PMA (Printed Monopole Antenna) with triple stop bands. The antenna is used for UWB application. The antenna is stopped the WiMAX (Worldwide Interoperability for Microwave Access), WLAN (Wireless Local Area Network) and ITU (International Telecommunication Union) bands. The antenna dimensions are 30x28x16 mm3. FR4 is used between ground and radiating patch with relative permittivity of 4.4. The VSWR (Voltage Standing Wave Ratio) is less than 2 between 3-11 GHz except WiMAX (3.1-3.7 GHz), WLAN (5.1-5.8 GHz) and the ITU frequency band (7.95-8.4 GHz). The antenna is design in CST software

Solving convex and non-convex static and dynamic economic dispatch problems using hybrid particle multi-swarm optimization

Problem ekonomične otpreme opterećenja ranije se uspješno rješavalo tehnikama rojeva. Međutim, elektroenergetski sustavi složenog ponašanja još uvijek čekaju razvoj robustnog algoritma za njihovu precizniju optimizaciju. Problem ekonomične otpreme uz ograničenja kao što su ograničenja generiranja, ukupna potražnja energije, ograničenja brzine pristupa i zabranjene operativne zone, čini problem složenijim za rješavanje čak i globalnim tehnikama. Za prevladavanje tih komplikacija, predlaže se novi algoritam pod nazivom Hybrid Particle Multi-Swarm Optimization (HPMSO). Predloženi algoritam ima svojstvo dubokog pretraživanja s prilično brzim odzivom. Vrednovanje učinkovitosti predloženog pristupa ispitivalo se konveksnim i ne-konveksnim funkcijama troškova uz ograničenja jednakosti i nejednakosti. Štoviše, slučajevi dinamičke ekonomične otpreme također su bili uključeni u statistička istraživanja za testiranje predloženog pristupa čak i u stvarnom vremenu. Različite studije slučaja provedene su korištenjem standardnih sustava za ispitivanje statičke i dinamičke otpreme. Usporedba predloženog pristupa i prethodnih tehnika pokazala je da se predloženim algoritmom postižu bolji rezultati.Economic Load Dispatch problem has been previously solved successfully with swarm techniques. However, power systems with complex behaviours still await a robust algorithm to be developed for their optimization more precisely. Economic Dispatch problem with constraints such as generator limits, total power demand, ramp rate limits and prohibited operating zones, makes the problem more complicated to solve even for global techniques. To overcome these complications, a new algorithm is proposed called Hybrid Particle Multi-Swarm Optimization (HPMSO). The proposed algorithm has a property of deep search with quite fast response. Convex and Non-convex cost functions along with equality and inequality constraints have been used to evaluate performance of proposed approach. Moreover, Dynamic Economic Dispatch cases have also been included in statistical studies to test the proposed approach even in real time. Different case studies have been accomplished using standard test systems of Static and Dynamic Economic Dispatch. Comparison of proposed approach and previous techniques show that the proposed algorithm has a better performance

Computational Seismic Analysis of Dry-Stack Block Masonry Wall

In this research the computational modeling of Dry-Stack Block Masonry (DSM) walls subjected to cyclic monotonic loading testing is done. The analytical results were compared with experimental test results of the unreinforced and unconfined DSM cantilever walls subjected to lateral loading along with a constant axial load. ABAQUS has been used for Finite Element Modeling and analysis of the wall. Various material properties are defined for the wall in the software and modeled as a homogeneous material. The proposed numerical models had a good correlation with the experimental data. The test results discussion includes failure moods, load displacement curves, and stress/strain profile. Doi: 10.28991/cej-2021-03091668 Full Text: PD