Taylor‐AMS features and deep convolutional neural network for converting nonaudible murmur to normal speech

Abstract

Communication becomes effective when the speech signal arrives with the profound characteristics. This insisted the researchers to develop an automatic system of recognizing the speech signals from the murmurs. Some of the traditional automatic recognition systems are unfit for the silent environments imposing a need for an effective recognition system. Also, the traditional automatic recognition methods, like Neural Networks, render poor performance in the presence of the murmurs. Thus, this article proposes a method for automatic whisper recognition using the Deep Convolutional Neural Network (DCNN). The training of the DCNN is performed using the proposed Stochastic‐Whale Optimization Algorithm (Stochastic‐WOA), which is designed by the integration of Stochastic Gradient Descent algorithm with WOA. The input to the classifier is the features that include pitch chroma, spectral centroid, spectral skewness, and Taylor‐Amplitude Modulation Spectrogram (Taylor‐AMS), which is obtained by combining Taylor series and Amplitude Modulation Spectrogram (AMS) features, of the preprocessed input speech signal. The experimentation of the method is performed using the real database and the analysis proves that the proposed method acquired a maximal accuracy of 0.9723, minimal False Positive Rate of 0.0257, and maximal True Positive Rate of 0.9981, respectively.

Mathematical engineering modeling and optimization of convolutional neural networks for common data areas in multitasking systems

“…Therefore, the simultaneous application of the EM algorithm and DBN model to learn the system state mapping relationship is worthy of research. However, the existing EM algorithm is only suitable for estimating time-varying parameters, and the time-domain consistency of the parameter estimation results cannot be guaranteed when used for node CPT parameter estimation [20]. On the other hand, in the case of scarcity of available system state monitoring data, the state monitoring information at different moments is correlated.…”

An Approach for Malicious JavaScript Detection Using Adaptive Taylor Harris Hawks Optimization-Based Deep Convolutional Neural Network

“…The considered features from the JavaScript Malware Collection dataset include executiontime,functioncalls,conditionalstatements,breakstatements,andBoolean.Thefeatures extractedfromthedatasetsaresubjectedtothedatatransformationforwhichthelogtransformation isapplied.Then,thefeatureselectionstepisadaptedinwhichtransformeddataaresubjectedtothe mutualinformation(Learned-Miller,2013)insuchawaythatonlysignificantfeaturesareacquired, whichisthenusedforthedetectionprocess.Finally,maliciousJavaScriptdetectionisperformed usingtheselectedfeaturesforwhichtheproposedadaptiveTaylor-HHO-basedDCNNisemployed. Here,theDCNN (Babu,et al,2016) (Kumar,et al,2020)classifierareoptimallytrainedusingthe proposedadaptiveTaylor-HHOalgorithmthatisdevelopedthroughtheintegrationoftheadaptive theoryintheTaylor-HHOalgorithm,whichisalreadydevelopedbyintegratingTaylorseries (Mangai, et al,2014)intheHHO (Heidaria,et al,2019).TheoutputoftheproposedmaliciousJavaScript detectiontechniqueliesintwoclasses,namelynormalandmaliciousJavaScript.Figure1portrays theSchematicviewoftheproposedAdaptive-Taylor-HHO-basedDCNNformaliciousJavaScript detection.…”

“…TheproposedAdaptiveTaylor-HHO-basedDCNNisemployedtodetectthemaliciousJavaScript codes.Here,theAdaptiveTaylor-HHO-basedDCNNisdevisedbyintegratingAdaptiveTaylor-HHO intheDCNNmodel,forchoosingoptimumweightspresentintheDCNN.TheproposedAdaptive Taylor-HHOisemployedforoptimizingDCNN (Babu,et al,2016) (Kumar,et al,2020)byselecting optimumweights.ThearchitectureofDCNNandtheprocessoftrainingareportrayedbelow.…”

“…ThestructuraldesignoftheDCNNisportrayedinfigure2.TheDCNN (Babu,et al,2016) (Kumar, et al,2020) ReLU:ReLUisanactivationfunctiontofacilitateefficiencyandminimalismandtheimplication oftheReLUlayeristhatDCNNwithReLUworksquickerwhilehandlinglargenetworks.Theoutput fromtheReLUlayerwhenfedtothefeaturemapsisexpressedas,…”

Design and evaluation of multimodal learning resources in blended teaching of college English

“…The resultant paradigm transformation focuses on Blended Learning (BL). Online features have been integrated into standard educational institutions in order to develop an active and customized classroom [33][34][35][36][37][38] . This teaching method emphasizes that the educational experience is not limited to one source and emphasizes modern technology to improve and differentiate learning.…”