haydar ankışhan - Academia.edu (original) (raw)
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Papers by haydar ankışhan
Acoustic signals having pathological problem are difficult to discriminate from each other. Despi... more Acoustic signals having pathological problem are difficult to discriminate from each other. Despite the presence of many features, the difficulties arise from the chaotic and nonlinear nature of these voices. Unlike the existing features, a new feature and feature space are emphasized in this study. Considering the maximum and minimum values of acoustic signals at certain time intervals, the relation between them is revealed and Max-Min space is created. Experimental studies have shown that the space distribution between pathological and normal sounds is completely separated from each other and that the space-scattering field sizes are different from each other. As a result of the studies, a time-based feature is introduced which allows the separation of chaotic and nonlinear acoustic signals in the literature.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2016
Computational and Mathematical Methods in Medicine, 2013
Istanbul University - Journal of Electrical & Electronics Engineering
Applied Acoustics
Abstract We introduce a new feature matrix (FMx) to discriminate the acoustic signals with the he... more Abstract We 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.
Biomedical Signal Processing and Control
Abstract This paper introduces a new hybrid feature vector for revealing the relationship between... more Abstract This 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.
Signal and Image Processing, 2011
Biomedical Signal Processing and Control
Signal and Image Processing, 2011
Acoustic signals having pathological problem are difficult to discriminate from each other. Despi... more Acoustic signals having pathological problem are difficult to discriminate from each other. Despite the presence of many features, the difficulties arise from the chaotic and nonlinear nature of these voices. Unlike the existing features, a new feature and feature space are emphasized in this study. Considering the maximum and minimum values of acoustic signals at certain time intervals, the relation between them is revealed and Max-Min space is created. Experimental studies have shown that the space distribution between pathological and normal sounds is completely separated from each other and that the space-scattering field sizes are different from each other. As a result of the studies, a time-based feature is introduced which allows the separation of chaotic and nonlinear acoustic signals in the literature.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2016
Computational and Mathematical Methods in Medicine, 2013
Istanbul University - Journal of Electrical & Electronics Engineering
Applied Acoustics
Abstract We introduce a new feature matrix (FMx) to discriminate the acoustic signals with the he... more Abstract We 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.
Biomedical Signal Processing and Control
Abstract This paper introduces a new hybrid feature vector for revealing the relationship between... more Abstract This 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.
Signal and Image Processing, 2011
Biomedical Signal Processing and Control
Signal and Image Processing, 2011