Gedizlioğlu, Çınar
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Gedizlioglu, Cinar
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cinar.gedizlioglu@ieu.edu.tr
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05.05. Computer Engineering
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| Journal | Count |
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| Asian Journal of Psychiatry | 1 |
| Frontiers in Psychiatry | 1 |
| Musicae Scientiae | 1 |
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4 results
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Article A Novel Approach to Depression Detection Using POV Glasses and Machine Learning for Multimodal Analysis(Frontiers Media SA, 2025) Kayis, Hakan; Celik, Murat; Kardes, Vildan Cakir; Karabulut, Hatice Aysima; Ozkan, Ezgi; Gedizlioglu, Cinar; Atasoy, NurayBackground Major depressive disorder (MDD) remains challenging to diagnose due to its reliance on subjective interviews and self-reports. Objective, technology-driven methods are increasingly needed to support clinical decision-making. Wearable point-of-view (POV) glasses, which capture both visual and auditory streams, may offer a novel solution for multimodal behavioral analysis.Objective This study investigated whether features extracted from POV glasses, analyzed with machine learning, can differentiate individuals with MDD from healthy controls.Methods We studied 44 MDD patients and 41 age/sex-matched HCs (18-55 years). During semi-structured interviews, POV glasses recorded video and audio data. Visual features included gaze distribution, smiling duration, eye-blink frequency, and head movements. Speech features included response latency, silence ratio, and word count. Recursive feature elimination was applied. Multiple classifiers were evaluated, and the primary model-ExtraTrees-was assessed using leave-one-out cross-validation.Results After Bonferroni correction, smiling duration, center gaze and happy face duration showed significant group differences. The multimodal classifier achieved an accuracy of 84.7%, sensitivity of 90.9%, specificity of 78%, and an F1 score of 86%.Conclusions POV glasses combined with machine learning successfully captured multimodal behavioral markers distinguishing MDD from controls. This low-burden, wearable approach demonstrates promise as an objective adjunct to psychiatric assessment. Future studies should evaluate its generalizability in larger, more diverse populations and real-world clinical settings.Doctoral Thesis Modülasyona Uyarlanabilir Sistemlerde Yerel Gam Algılama Yoluyla Müzik Akorları Üretme(2025) Gedizlioğlu, Çınar; Erol, KutluhanAkor üretimi, statik, kural tabanlı sistemlerden, degi¸sken armonileri temsil etmekte ˘ oldukça fazla potansiyeli olan karma¸sık makine ögrenme modellerine kadar istikrarlı ˘ bir ¸sekilde geli¸smi¸stir. ˙Ilk modeller esneklik veya uyarlanabilirlik potansiyelini kısıtlayan katı ¸sablonlara dayanıyordu. Modern veri odaklı yakla¸sımlar karma¸sık armonik ili¸skileri yakalamada geli¸sme gösterse de, modülasyonları dahil etmekte ve uzun menzilli müzikal bagımlılıkları korumakta yetersiz kalmakta ve genellikle ˘ gam tespitinin çok basitle¸stirilmi¸s biçimlerine ba¸svurmaktadır. Bu tez, belirli bir melodi üzerinde akor üretme ile ilgilidir ve yukarıda belirtilen ana sınırlamaları, bagımsız olarak e ˘ gitilmi¸s ve ince ayarlanmı¸s bile¸senleri içeren yeni bir yakla¸sımla ˘ a¸smayı hedeflemi¸stir. Bu bile¸senler bir yerel gam algılama algoritması ve bir akor üretme modelinden olu¸sur. Yerel gam tespit algoritması melodi içindeki tonalite kaymalarını/modülasyonları dinamik olarak tespit ederken, akor üretim modeli degi¸sen ˘ tonal baglama göre armonik olarak tutarlı akor geçi¸sleri üretir. Bu unsurlar ba ˘ gımsız ˘ olarak egitildikten ve optimize edildikten sonra, hem uzun menzilli ba ˘ gımlılıklar ˘ arasında tutarlılıgı koruyan, hem de tonalitedeki de ˘ gi¸sikliklere dinamik uyarlamalar ˘ yapan tek bir bile¸sik modelde birle¸stirilirler. Bu tez, alı¸sılagelmi¸s yapıları modüler ve uyarlanabilir hale getirerek mevcut akor üretim sistemlerindeki dezavantajlara etkili bir çözüm önermektedir. Bu sistemin son versiyonunun iyi bilinen parçaların melodileri üzerinde yeterli performans gösterdigi gösterilirken, bu performansı geli¸stirme adına ˘ potansiyel yollar tartı¸sılmaktadır. Anahtar Kelimeler: yerel gam saptama, sembolik gam bulma, akor üretimi, modülasyon, makina ögrenmesi, müzik bilgi çıkarımıArticle A New Approach in Autism Diagnosis: Evaluating Natural Interaction Using Point of View (POV) Glasses(Elsevier, 2026) Kayis, Hakan; Celik, Murat; Gedizlioglu, Cinar; Kayis, Elif; Aydemir, Cumhur; Hatipoglu, Arda; Ozbaran, BurcuThis study introduces an AI-assisted method based on examiner-worn Point of View (POV) glasses and computer vision analysis to provide objective behavioral data for the diagnosis of Autism Spectrum Disorder (ASD). The study included 29 children with ASD and 27 children without ASD, aged between 17 and 36 months. During semi-structured naturalistic interactions, the examiner wore POV glasses equipped with a scene camera that captured the child's face from an eye-level perspective, preserving ecological validity. Behavioral parameters-including facial expressions, approximate social gaze (operationalized as the child's eyes orientation toward the POV camera), and head mobility-were extracted using OpenFace and MediaPipe and subsequently analyzed with machine learning techniques. Statistical analyses revealed that total social gaze duration, longest social gaze, social smiling, number of responses to name, response latency, response duration, social responsiveness, and head movements along the z-axis had p-values <= 0.05, while head movements on the x- and y-axes, total head movement, and rapid head movements had p-values > 0.05. The classification model developed using decision trees and the AdaBoost algorithm demonstrated high performance, achieving an accuracy of 91.07 % and a sensitivity of 89.65 %. These findings support the clinical applicability of examiner-worn POV recordings for early ASD detection and highlight their potential to complement traditional, subjective assessment methods.Article A Regularization Algorithm for Local Key Detection(Sage publications ltd, 2024) Gedizlioğlu, Çınar; Erol, KutluhanIn the field of music information retrieval, the detection of global key in both popular and classical music has been studied extensively, but local key detection has been studied to a lesser extent, even though modulation is an important component of compositional style. It is particularly challenging to identify key change boundaries correctly. We modeled this task as an optimization problem, that of finding out how to divide a piece into sections in different keys taking into consideration both the quality of the fit between the key and the section and the number of sections. We determined the optimal assignment of key to section using the Krumhansl-Schmuckler algorithm with a slightly modified version of the Krumhansl and Kessler key profile. We included a regularization algorithm in the formulation of our problem to balance the number of sections and avoid superfluous modulations. Using a dataset of 80 randomly chosen pieces of music in a variety of genres and levels of complexity, we compared our algorithm with a hidden Markov model (HMM) to determine which method is better for identifying local key. Our approach yielded significantly more accurate results and suggests future avenues of research.
