1. A hybrid measure for the discrimination of the acoustic signals: Feature matrix (FMx)
- Author
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Sıtkı Çağdaş İnam and Haydar Ankishan
- Subjects
010302 applied physics ,Acoustics and Ultrasonics ,Computer science ,business.industry ,Feature extraction ,Pattern recognition ,Fundamental frequency ,Quadratic classifier ,01 natural sciences ,Convolutional neural network ,Measure (mathematics) ,Support vector machine ,Feature (computer vision) ,Histogram ,0103 physical sciences ,Artificial intelligence ,business ,010301 acoustics - 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.
- Published
- 2019
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