Teknik Bilimler Meslek Yüksekokulu / Vocational School of Technical Sciences
Permanent URI for this collectionhttps://hdl.handle.net/11727/2031
Browse
3 results
Search Results
Item Classification of acoustic signals with new feature: Fibonacci space (FSp)(2019) Ankishan, Haydar; 0000-0002-6240-2545; AAH-4421-2019In this study, a new feature and feature space (FSp) are introduced by using the approach of Fibonacci series formation. The results are presented as two experimental studies. The nine groups of acoustic signals and pathological human voices are investigated in the first and second experiments, respectively. Convolutional Neural Network (CNN) and Multi-Class Support Vector Machines (M-SVMs) are used to figure out the effect of the proposed feature and its FSp on the classification accuracy. It is observed that the proposed feature and its formed space yield significant results for the discrimination of those signals. Experimental studies show that the classification accuracy of test data is increased by 5.3% when the proposed feature is used with CNN and M-SVMs. In addition, each acoustic group is significantly discriminated in both experimental studies. It is concluded that the proposed feature and its space can be used as a temporal feature for different purposes such as automatic speech recognition, pattern recognition, and emotional voice discrimination etc. (C) 2018 Elsevier Ltd. All rights reserved.Item Estimation of heartbeat rate from speech recording with hybrid feature vector (HFV)(2019) Ankishan, Haydar; 0000-0002-6240-2545; AAH-4421-2019This paper introduces a new hybrid feature vector for revealing the relationship between human voice and heartbeat rate (HBR). Various samples of the sustained vowel /a/ for different HBR have been extracted from a database which is created for this study. A convolutional neural network (CNN)-Regression (R), support vector machines (SVMs)-Regression (R), and multiple linear regression (MLR) are used as regression models. The experimental results show that the percentage of predictions within an acceptable error margin has been obtained as 98.92% for CNN-R, 98.70% for SVMs-R and 96.88% for MLR when Forward Sequential is used as a feature selection algorithm. The results also reveal that the CNN-R (root mean square error (RMSE) =0.3909) has produced better prediction values in estimating HBR than those produced by SVMs-R (RMSE=0.4277) and MLR (RMSE =0.4449). As a result, it is seen that the extracted hybrid feature vector provides a novel relationship between human voice and HBR. (C) 2019 Elsevier Ltd. All rights reserved.Item A hybrid measure for the discrimination of the acoustic signals: Feature matrix (FMx)(2019) Anskishan, Haydar; Inam, Sitki Cagdas; 0000-0002-6240-2545; 0000-0003-0820-9186; AAH-4421-2019We introduce a new feature matrix (FMx) to discriminate the acoustic signals with the help of their hybrid characteristics. The FMx has hybrid domain characteristics consisting of feature values such as distributional area (polygonal area), maximum values of the histogram and fundamental frequency of the difference-difference (d2d) vector. To show the performance of the FMx, three different datasets are used together with quadratic discriminant analysis (QDA), multiclass support vector machines (M-SVMs) and convolutional neural networks (CNN). The simulation results show that FMx provides effective and useful information for the discrimination of the signals into subclasses with the help of ReliefF and forward sequential algorithms. In simulations, the test accuracies with QDA, M-SVMs and CNN were obtained as 94.20%, 100% and 100% respectively. So, the results of the simulations support the effectiveness of the FMx for the acoustic signal classification with three different datasets compared to the previous studies. (C) 2019 Elsevier Ltd. All rights reserved.