Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Author: search.filters.author.Sert, Mustafasearch.filters.applied.f.publicationcategory: search.filters.publicationcategory.Makale - Ulusal Hakemli Dergi

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    Analysis of Deep Neural Network Models for Acoustic Scene Classification
    (2019) Basbug, Ahmet Melih; Sert, Mustafa
    Acoustic Scene Classification is one of the active fields of both audio signal processing and machine learning communities. Due to the uncontrolled environment characteristics and the multiple diversity of environmental sounds, the classification of acoustic environment recordings by computer systems is a challenging task. In this study, the performance of deep learning algorithms on acoustic scene classification problem which includes continuous information in sound events are analyzed. For this purpose, the success of the AlexNet and the VGGish based 4- and 8-layered convolutional neural networks utilizing long-short-term memory recurrent neural network (LSTM-RNN) and Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) architectures have been analyzed for this classification task. In this direction, we adapt the LSTM-RNN and the GRU-RNN models with the 4- and 8-layared CNN architectures for the classification. Our experimental results show that 4-layered CNN with GRU structure improve the accuracy.
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    Classification of Obstructive Sleep Apnea using Multimodal and Sigma-based Feature Representation
    (2019) Memis, Gokhan; Sert, Mustafa
    Obstructive sleep apnea (OSA) is a sleep disorder characterized by a decrease in blood oxygen saturation and waking up after a long time. Diagnosis can be made by following a full night with a polysomnogram device, so there is a need for computer-based methods for the diagnosis of OSA. In this study, a method based on feature selection is proposed for OSA classification using oxygen saturation and electrocardiogram signals. Standard deviation (sigma) based features have been created to increase accuracy and reduce computational complexity. To evaluate the effectiveness, comparisons were made with selected machine learning algorithms. The achievements of the obtained features were compared with Naive Bayes (NB), k-nearest neighborhood (kNN) and Support Vector Machine (SVM) classifiers. The tests performed on the PhysioNet dataset consisting of real clinical samples show that the use of sigma-based features result an average performance increase of 1.98% in all test scenarios.