Yazar "Sahin, Sakir Engin" seçeneğine göre listele

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    Automated speech emotion polarization for a distance education system based on orbital local binary pattern and an appropriate sub-band selection technique
    (Springer, 2023) Tanko, Dahiru; Demir, Fahrettin Burak; Dogan, Sengul; Sahin, Sakir Engin; Tuncer, Turker
    The distance education system was widely adopted during the Covid-19 pandemic by many institutions of learning. To measure the effectiveness of this system, it is essential to evaluate the performance of the lecturers. To this end, an automated speech emotion recognition model is a solution. This research aims to develop an accurate speech emotion recognition model that will check the lecturers/instructors' emotional state during lecture presentations. A new speech emotion dataset is collected, and an automated speech emotion recognition (SER) model is proposed to achieve this aim. The presented SER model contains three main phases, which are (i) feature extraction using multi-level discrete wavelet transform (DWT) and one-dimensional orbital local binary pattern (1D-OLBP), (ii) feature selection using neighborhood component analysis (NCA), (iii) classification using support vector machine (SVM) with ten-fold cross-validation. The proposed 1D-OLBP and NCA-based model is tested on the collected dataset, containing three emotional states with 7101 sound segments. The presented 1D-OLBP and NCA-based technique achieved a 93.40% classification accuracy using the proposed model on the new dataset. Moreover, the proposed architecture has been tested on the three publicly available speech emotion recognition datasets to highlight the general classification ability of this self-organized model. We reached over 70% classification accuracies for all three public datasets, and these results demonstrated the success of this model.
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    PrismPatNet: Novel prism pattern network for accurate fault classification using engine sound signals
    (Wiley, 2023) Sahin, Sakir Engin; Gulhan, Gokhan; Barua, Prabal Datta; Tuncer, Turker; Dogan, Sengul; Faust, Oliver; Acharya, U. Rajendra
    Engines are prone to various types of faults, and it is crucial to detect and indeed classify them accurately. However, manual fault type detection is time-consuming and error-prone. Automated fault type detection promises to reduce inter- and intra-observer variability while ensuring time invariant attention during the observation duration. We have proposed an automated fault-type detection model based on sound signals to realize these advantageous properties. We have named the detection model prism pattern network (PrismPatNet) to reflect the fact that our design incorporates a novel feature extraction algorithm that was inspired by a 3D prism shape. Our prism pattern model achieves high accuracy with low-computational complexity. It consists of three main phases: (i) prism pattern inspired multilevel feature generation and maximum pooling operator, (ii) feature ranking and feature selection using neighbourhood component analysis (NCA), and (iii) support vector machine (SVM) based classification. The maximum pooling operator decomposes the sound signal into six levels. The proposed prism pattern algorithm extracts parameter values from both the signal itself and its decompositions. The generated parameter values are merged and fed to the NCA algorithm, which extracts 512 features from that input. The resulting feature vectors are passed on to the SVM classifier, which labels the input as belonging to 1 of 27 classes. We have validated our model with a newly collected dataset containing the sound of (1) a normal engine and (2) 26 different types of engine faults. Our model reached an accuracy of 99.19% and 98.75% using 80:20 hold-out validation and 10-fold cross-validation, respectively. Compared with previous studies, our model achieved the highest overall classification accuracy even though our model was tasked with identifying significantly more fault classes. This performance indicates that our PrismPatNet model is ready to be installed in real-world applications.