Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders

Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals. The success of many existing systems is therefore largely dependent on the choice of features used for training. In this work, we introduce a novel multi-channel, multi-resolution convolutional auto-encoder neural network that works on raw time-domain signals to determine appropriate multi-resolution features for separating the singing-voice from stereo music. Our experimental results show that the proposed method can achieve multi-channel audio source separation without the need for hand-crafted features or any pre- or post-processing

Partial discharge behavior under operational and anomalous conditions in HVDC systems

Power cables undergo various types of overstressing conditions during their operation that influence the integrity of their insulation systems. This causes accelerated ageing and might lead to their premature failure in severe cases. This paper presents an investigation of the impacts of various dynamic electric fields produced by ripples, polarity reversal and transient switching impulses on partial discharge (PD) activity within solid dielectrics with the aim of considering such phenomena in high voltage direct current (HVDC) cable systems. Appropriate terminal voltages of a generic HVDC converter were reproduced - with different harmonic contaminations - and applied to the test samples. The effects of systematic operational polarity reversal and superimposed switching impulses with the possibility of transient polarity reversal were also studied in this investigation. The experimental results show that the PD is greatly affected by the dynamic changes of electric field represented by polarity reversal, ripples and switching. The findings of this study will assist in understanding the behaviour of PDs under HVDC conditions and would be of interest to asset managers considering the effects of such conditions on the insulation diagnostics

Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation

In deep neural networks with convolutional layers, each layer typically has fixed-size/single-resolution receptive field (RF). Convolutional layers with a large RF capture global information from the input features, while layers with small RF size capture local details with high resolution from the input features. In this work, we introduce novel deep multi-resolution fully convolutional neural networks (MR-FCNN), where each layer has different RF sizes to extract multi-resolution features that capture the global and local details information from its input features. The proposed MR-FCNN is applied to separate a target audio source from a mixture of many audio sources. Experimental results show that using MR-FCNN improves the performance compared to feedforward deep neural networks (DNNs) and single resolution deep fully convolutional neural networks (FCNNs) on the audio source separation problem.Comment: arXiv admin note: text overlap with arXiv:1703.0801

Evaluation of antimicrobial activity of a lichen used as a spice (Platismatia glauca)

Background: Lichen is a complex symbiotic relationship arose from algae or cyanobacteria that live together with some fungal species. Some of them are edible and consumed as spice such asPlatismatia glauca. The current study aimed to evaluate it’s the antimicrobial properties of the methanolic extract of lichen thalli of P. glauca against some referenced bacterial and yeast strains.Methods: Disc diffusion test, minimum inhibitory (MIC) and minimum bactericidal (MBC) or minimum fungicidal (MFC) tests were carried out to evaluate the antimicrobial activity of lichen.Results: All tested microorganisms exhibited varying degrees of susceptibility. Among the tested strains, the most susceptible bacterium -using the disc diffusion assay- was Staphylococcus saprophyticus (18.5±1.0 mm), followed by Staphylococcus aureus (14.5±0.5 mm), Shigella flexneri(12.5±1.5 mm), Streptococcus pneumoniae (12.0±1.0 mm), Proteus vulgaris (11.5±0.5 mm),Salmonella Typhimurium (11.5±0.5 mm), Bacillus cereus (11.0±1.0 mm) and Escherichia coli(11.0±0.0 mm), respectively. It also showed high antifungal activity against Candida albicans(22.5±0.5 mm).  The MIC, MBC and MFC were promising, which were as low as 3.125 to 12.5 mg/ml for MIC and 6.25 to 12.5 mg/ml for MBC and MFC.Conclusion: From the obtained results, it is concluded that the lichen thalli of Platismatia glaucapossesses a remarkable antimicrobial activity and it may be considered as a source of potential antimicrobial agents.Keywords: Antibacterial, Antifungal, Lichen, Platismatia glauc

Modeling and Control of Wind Turbine to Damp the Power Oscillation

Damping inter-area oscillation by using a permanent magnet synchronous generator (PMSG) wind turbine is considered. The PMSG wind turbine is connected to the IEEE-30 bus power system at different buses. H-infinity design controller is proposed to modulate the power where the input of the H-infinity control is the variation of the local grid generator speed and the output is feedback to activate the PMSG speed control, blade pitch angle control and dc voltage control. MATLAB/SIMULINK is used in this study. The IEEE-30 bus system is reduced to 7 buses based on the number of generators to simplify the stability study. The method is applied to a seven-area power system that exhibits undamped oscillations. Results presented in this study demonstrate the effectiveness of the wind generator in increasing system damping considerably