Voice Separation and Enhancement (VoSE) algorithm aims at designing a predictive model to solve the problem of speech enhancement and separation from a mixed signal. VoSE can be used for any language, with or without a large Datasets. VoSE can be utilized by any voice response system like, Siri, Alexa, Google Assistant which as of now work on single voice command. The pre-processing of the voice is done using a Trimming Negative and Nonzero voice filter (TNNVF), designed by the authors. TNNVF is independent of language, it works on any voice signal. The segmentation of a voice is generally carried out on frequency domain or time domain. Independently they are known to have ripple or rising effect. To rule out the ripple effect, data is filtered in the time-frequency domain. Voice print of the entire sound files is created for the training and testing purpose. 80% of the voice prints are used to train the network and 20% are kept for testing. The training set contains over 48,000 voice prints. LightGBM with TensorFlow helps in generating unique voice prints in a short time. To enhance the retrieved voice signals, Enhance Predictive Voice(EPV) function is designed. The tests are conducted on English and Indian languages. The proposed work is compared with K-means, Decision Stump, Naïve Bayes, and LSTM.
Evolutionary algorithms are inspired by the biological model of evolution and natural selection and are used to solve computationally hard problems, also known as NP-hard problems. The main motive to use these algorithms is their robust and adaptive nature to provide best search techniques for complex problems. This paper presents a comparative analysis of classification of algorithm's family instead of algorithms comparison using KEEL tool. This work compares SSMA-C, DROP3PSO-C, FURIA-C, GFS-MaxLogitBoost-Cand CPSO-C algorithms. Further, these selected evolutionary algorithms are compared against two statistical classifiers using the Wilcoxon signed rank test and Friedman test on following datasets—bupa, ecoli, glass, haberman, iris, monks, vehicle, and wine—to calculate classification efficiencies of these algorithms. Experimental results reveal some differences among these algorithms. Visualization module in the model has been used to give overall results as a summary while statistical test using Clas-Wilcoxin-ST compared the algorithms in a pair-wise fashion to conclude experimental findings.
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