Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
This paper mainly focuses on the recent advances in the semi-analytical approximated methods for solving a system of Volterra integro-differential equations of the second kind by using Adomian Decomposition Method (ADM), Variational Iteration Method (VIM) and Homotopy Perturbation Method (HPM). Convergence analysis of the exact solution of the proposed methods is established. To illustrate the methods, an example is presented
Speaker recognition is a research topic that is still interesting and challenging. Various problems such as noise problems, poor performance, short duration, spoofing and inconsistency are problems that need to be resolved immediately. The researchers conducted research with various models from traditional methods such as the Gaussian Mixture Model (GMM), Support Vector Machine (SVM) and Hidden Markov Model (HMM) to the Deep Learning methods using Deep Neural Network (DNN) and Convolutional Neural Network (CNN). In addition, various hybrid deep learning methods are also used. Various papers that use these methods are difficult to understand, especially when compared between one method with another to obtain novelty and direction of research on speaker recognition. Systematic Literature Review (SLR) is helpful in identifying and interpreting various findings in a field of research in answering the research questions that have determined. This paper uses SLR in identifying research trends,datasets, feature extraction ,classification methods and evaluation techniques used in speaker recognition using deep learning. Results of the SLR discussion are 82 major study journals from 2011 to 2019 show that 20% of research studies focus on speaker verification topics, 11.5% each at Speaker Recognition in Noisy Conditions, Speaker Emotion Recognition and Short and Mismatch Utterance Duration. Research in speaker recognition 90% used public datasets and 10% used private datasets. The MFCC method is a method often used in feature extraction although there are I-vector and X-Vector methods that are starting to be used in deep learning. Deep Neural Network is a classification method that is often used in speaker recognition. 31% of the evaluation techniques that are often used are Equal Error Rate, 29% used the Word Error Rate and 40% used others method such as Accuracy, Root Mean Square Error (RMSE), Signal to Noise Ratio (SNR), Character Error Rate (CER) , Phone Error Rate (PER) and Speech Separation Performance (SSP)