Deep neural network techniques for monaural speech enhancement: state of the art analysis

Deep neural networks (DNN) techniques have become pervasive in domains such as natural language processing and computer vision. They have achieved great success in these domains in task such as machine translation and image generation. Due to their success, these data driven techniques have been applied in audio domain. More specifically, DNN models have been applied in speech enhancement domain to achieve denosing, dereverberation and multi-speaker separation in monaural speech enhancement. In this paper, we review some dominant DNN techniques being employed to achieve speech separation. The review looks at the whole pipeline of speech enhancement from feature extraction, how DNN based tools are modelling both global and local features of speech and model training (supervised and unsupervised). We also review the use of speech-enhancement pre-trained models to boost speech enhancement process. The review is geared towards covering the dominant trends with regards to DNN application in speech enhancement in speech obtained via a single speaker.Comment: conferenc

Speech Separation based on Contrastive Learning and Deep Modularization

The current monaural state of the art tools for speech separation relies on supervised learning. This means that they must deal with permutation problem, they are impacted by the mismatch on the number of speakers used in training and inference. Moreover, their performance heavily relies on the presence of high-quality labelled data. These problems can be effectively addressed by employing a fully unsupervised technique for speech separation. In this paper, we use contrastive learning to establish the representations of frames then use the learned representations in the downstream deep modularization task. Concretely, we demonstrate experimentally that in speech separation, different frames of a speaker can be viewed as augmentations of a given hidden standard frame of that speaker. The frames of a speaker contain enough prosodic information overlap which is key in speech separation. Based on this, we implement a self-supervised learning to learn to minimize the distance between frames belonging to a given speaker. The learned representations are used in a downstream deep modularization task to cluster frames based on speaker identity. Evaluation of the developed technique on WSJ0-2mix and WSJ0-3mix shows that the technique attains SI-SNRi and SDRi of 20.8 and 21.0 respectively in WSJ0-2mix. In WSJ0-3mix, it attains SI-SNRi and SDRi of 20.7 and 20.7 respectively in WSJ0-2mix. Its greatest strength being that as the number of speakers increase, its performance does not degrade significantly.Comment: arXiv admin note: substantial text overlap with arXiv:2212.0036

Contrastive Environmental Sound Representation Learning

Machine hearing of the environmental sound is one of the important issues in the audio recognition domain. It gives the machine the ability to discriminate between the different input sounds that guides its decision making. In this work we exploit the self-supervised contrastive technique and a shallow 1D CNN to extract the distinctive audio features (audio representations) without using any explicit annotations.We generate representations of a given audio using both its raw audio waveform and spectrogram and evaluate if the proposed learner is agnostic to the type of audio input. We further use canonical correlation analysis (CCA) to fuse representations from the two types of input of a given audio and demonstrate that the fused global feature results in robust representation of the audio signal as compared to the individual representations. The evaluation of the proposed technique is done on both ESC-50 and UrbanSound8K. The results show that the proposed technique is able to extract most features of the environmental audio and gives an improvement of 12.8% and 0.9% on the ESC-50 and UrbanSound8K datasets respectively

A Forward-Looking Approach to Compare Ranking Methods for Sports

In this paper, we provide a simple forward-looking approach to compare rating methods with respect to their stability over time. Given a rating vector of entities involved in the comparison and a ranking indicated by the rating, the stability of the methods is measured by the change in rating vector and ranks of the entities over time from a forward-looking perspective. We investigate various linear algebraic rating methods and use the Euclidean distance and Kendall tau rank correlation to measure their stability in rating and ranking, respectively. The investigations are based on both rolling and expanding window approaches. We apply the methodology to sports as a widely known ranking and rating environment. The results suggest that PageRank and Massey rating methods provide better rating and ranking stability than simple methods, such as winning percentage, and more advanced ones, such as Colley’s least square and Keener’s eigenvector-based method. Finally, a simple way to examine the potential predictive power of the rating methods is also provided

MOLECULAR DOCKING AND PHARMACOKINETIC PREDICTION OF HERBAL DERIVATIVES AS MALTASE-GLUCOAMYLASE INHIBITOR

  Objective: To perform molecular docking and pharmacokinetic prediction of momordicoside F2, beta-sitosterol, and cis-N-feruloyltyramine herbal derivatives as maltase-glucoamylase (MGAM) inhibitors for the treatment of diabetes.Methods: The herbal derivatives and standard drug miglitol were docked differently onto MGAM receptor using AutoDock Vina software. In addition, Lipinski's rule, drug-likeness, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were analyzed using Molinspiration, ADMET structure–activity relationship, and prediction of activity spectra for substances online tools.Results: Docking studies reveal that momordicoside F2, beta-sitosterol, and cis-N-feruloyltyramine derivatives have high binding affinity to the MGAM receptor (−7.8, −6.8, and −6.5 Kcal/Mol, respectively) as compared to standard drug miglitol (−5.3 Kcal/Mol). In addition, all the herbal derivatives indicate good bioavailability (topological polar surface area <140 Ȧ and Nrot <10) without toxicity or mutagenic effects.Conclusion: The molecular docking and pharmacokinetic information of herbal derivatives obtained in this study can be utilized to develop novel MGAM inhibitors having antidiabetic potential with better pharmacokinetic and pharmacodynamics profile

Effect of Motivation on Performance of Employees in Privately Owned Firms: A Case Study of Mt. Longonot Medical Services Ltd, Kenya

Organizations in the modern world consistently strive to succeed in a highly competitive global market environment. However many organizations have not been able to develop strategies which offer competitive advantage and therefore find it difficult to cope with the emerging competitive threats. Managers should put in place good policies to avert the strong competitive forces. Poor management of human resource facilitated by lack of motivational policies are some of the problems most organizations face. Employers today would like to have their workers motivated and ready to work but do not understand what truly motivates a person.  This study explored the effect of motivation on performance of employees in privately owned firms, a case study of Mt. Longonot Medical Services Ltd. The objectives for this study were: to determine the effect of financial motivation on employee’s performance, and to evaluate the impact of non-financial motivators (e.g. job security, recognition and appreciation, promotion and growth, communication and decision making involvement, good working conditions, tools and equipment) on employee’s performance. This study was looking for answers to such questions as to whether financial motivation has an impact on the employees’ performance and whether non-financial motivators affect the employee output. This research was premised on the motivational theories and emphasized on the Maslow’s Hierarchy of needs theory. The research design was a survey and the target population constitutes sixty individuals. Questionnaires were used as the data collection method. Stratified sampling procedure was applied as well. Randomly selected samples of sixty respondents participated from an entire population of two hundred employees. The research was validated using triangulation and member checking approaches. Regression was used; SPSS model of data analysis was employed as a statistical techniques. This research highlighted the expected effects of employee motivation and established whether motivation boosts employee performance. The research is important in that it adds knowledge to the area of strategic human resource management which is of great value to managers, organizations and the government. The study is significant because it would help organizations identify and apply motivational policies to ensure high productivity for achievement of the organizational goals. The researcher suggests a more competitive remuneration, job security and promotion, equitable and fair staff development, provision of adequate tools and equipment, decision making involvement, enhancing effective communication and providing good working condition

Robust estimation of heritability and predictive accuracy in plant breeding: evaluation using simulation and empirical data

UID/MAT/00297/2019 UIDB/00297/2020 SFRH/BSAB/105935/2014 SFRH/BSAB/142919/2018 Project PT/A13/17-DE/57339863 Grant PI 377/18-1 Grant OG 83/1-1 & OG 83/1-2BACKGROUND: Genomic prediction (GP) is used in animal and plant breeding to help identify the best genotypes for selection. One of the most important measures of the effectiveness and reliability of GP in plant breeding is predictive accuracy. An accurate estimate of this measure is thus central to GP. Moreover, regression models are the models of choice for analyzing field trial data in plant breeding. However, models that use the classical likelihood typically perform poorly, often resulting in biased parameter estimates, when their underlying assumptions are violated. This typically happens when data are contaminated with outliers. These biases often translate into inaccurate estimates of heritability and predictive accuracy, compromising the performance of GP. Since phenotypic data are susceptible to contamination, improving the methods for estimating heritability and predictive accuracy can enhance the performance of GP. Robust statistical methods provide an intuitively appealing and a theoretically well justified framework for overcoming some of the drawbacks of classical regression, most notably the departure from the normality assumption. We compare the performance of robust and classical approaches to two recently published methods for estimating heritability and predictive accuracy of GP using simulation of several plausible scenarios of random and block data contamination with outliers and commercial maize and rye breeding datasets. RESULTS: The robust approach generally performed as good as or better than the classical approach in phenotypic data analysis and in estimating the predictive accuracy of heritability and genomic prediction under both the random and block contamination scenarios. Notably, it consistently outperformed the classical approach under the random contamination scenario. Analyses of the empirical maize and rye datasets further reinforce the stability and reliability of the robust approach in the presence of outliers or missing data. CONCLUSIONS: The proposed robust approach enhances the predictive accuracy of heritability and genomic prediction by minimizing the deleterious effects of outliers for a broad range of simulation scenarios and empirical breeding datasets. Accordingly, plant breeders should seriously consider regularly using the robust alongside the classical approach and increasing the number of replicates to three or more, to further enhance the accuracy of the robust approach.publishersversionpublishe

Machine Learning Algorithm to Detect Impersonation in an Essay-Based E-Exam

Essay-based E-exams require answers to be written out at some length in an E- learning platform The questions require a response with multiple paragraphs and should be logical and well-structured These type of examinations are increasingly becoming popular in academic institutions of higher learning based on the experience of COVID-19 pandemic Since the exam is mainly done virtually with reduced supervision the risk of impersonation and stolen content from other sources increases Due to this there is need to design cost effective and accurate techniques that are able to detect cheating in an essay based E- exa

A Special Structural Based Weighted Network Approach for the Analysis of Protein Complexes

The detection and analysis of protein complexes is essential for understanding the functional mechanism and cellular integrity. Recently, several techniques for detecting and analysing protein complexes from Protein–Protein Interaction (PPI) dataset have been developed. Most of those techniques are inefficient in terms of detecting, overlapping complexes, exclusion of attachment protein in complex core, inability to detect inherent structures of underlying complexes, have high false-positive rates and an enrichment analysis. To address these limitations, we introduce a special structural-based weighted network approach for the analysis of protein complexes based on a Weighted Edge, Core-Attachment and Local Modularity structures (WECALM). Experimental results indicate that WECALM performs relatively better than existing algorithms in terms of accuracy, computational time, and p-value. A functional enrichment analysis also shows that WECALM is able to identify a large number of biologically significant protein complexes. Overall, WECALM outperforms other approaches by striking a better balance of accuracy and efficiency in the detection of protein complexes

An efficient weighted network centrality approach for exploring mechanisms of action of the Ruellia herbal formula for treating rheumatoid arthritis

Aim This study outlines an efficient weighted network centrality measure approach and its application in network pharmacology for exploring mechanisms of action of the Ruellia prostrata (RP) and Ruellia bignoniiflora (RB) herbal formula for treating rheumatoid arthritis. Method In our proposed method we first calculated interconnectivity scores all the network targets then computed weighted centrality score for all targets to identify of major network targets based on centrality score. We apply our technology to network pharmacology by constructing herb-compound-putative target network; compound-putative targets-RA target network; and imbalance multi-level herb-compound-putative target-RA target-PPI network. We then identify the major targets in the network based on our centrality measure approach. Finally we validated the major identified network targets using the enrichment analysis and a molecular docking simulation. Result The results reveled our proposed weighted network centrality approach outperform classical centrality measure in identification of influential nodes in four real complex networks based on SI model simulation. Application of our approach to network pharmacology shows that 57 major targets of which 33 targets including 8 compositive compounds, 15 putative target and 10 therapeutic targets played an important role in the network and directly linked to rheumatoid arthritis. Enrichment analysis confirmed that putative targets were frequently involved in TNF, CCR5, IL-17 and G-protein coupled receptors signaling pathways which are critical in the progression of rheumatoid arthritis. The molecular docking simulation indicated four targets had significant binding affinity to major protein targets. Glyceryl diacetate-2-Oleate and Oleoyl chloride showed the best binding affinity to all targets proteins and were within Lipinski limits. ADMET prediction also confirm both compounds had no toxic effect on human hence potential lead drug compounds for treating rheumatoid arthritis. Conclusion This study developed an efficient weighted network centrality approach as tool for identification of major network targets. Network pharmacology findings provides promising results that could lead us to design and discover of alternative drug compounds. Though our approach is a purely in silico method, clinical experiments are required to test and validate the hypotheses of our computational methods