Author, Institution: Ahmad Qurthobi, Kaunas University of Technology
Science area, field of science: Technological Sciences, Informatics Engineering, T007
Research supervisor: Prof. Dr. Rytis Maskeliūnas (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007)
Dissertation Defence Board of Informatics Engineering Science Field:
Prof. Dr. Renaldas Urniežius (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007) – chairperson
Prof. Dr. Nikolaj Goranin (Vilnius Gediminas Technical University, Technological Sciences, Informatics Engineering, T007)
Assoc. Prof. Dr. Zenun Kastrati (Linnaeus University, Sweden, Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Renaldas Raišutis (Kaunas University of Technology, Technological Sciences, Measurement Engineering, T010)
Prof. Dr. Dmitrij Šešok (Vilnius Gediminas Technical University, Technological Sciences, Informatics Engineering, T007)
Dissertation defence meeting will be at Rectorate Hall of Kaunas University of Technology (K. Donelaičio 73–402, Kaunas)
The doctoral dissertation is available at the library of Kaunas University of Technology (Gedimino g. 50, Kaunas) and on the internet: A. Qurthobi el. dissertation.pdf
© A. Qurthobi, 2026 “The text of the thesis may not be copied, distributed, published, made public, including by making it publicly available on computer networks (Internet), reproduced in any form or by any means, including, but not limited to, electronic, mechanical or other means. Pursuant to Article 25(1) of the Law on Copyright and Related Rights of the Republic of Lithuania, a person with a disability who has difficulties in reading a document of a thesis published on the Internet, and insofar as this is justified by a particular disability, shall request that the document be made available in an alternative form by e-mail to doktorantura@ktu.lt.”
Annotation: This dissertation examines the challenge of robust audio classification in diverse acoustic environments where background noise and signal variability reduce recognition accuracy. The study aims to develop a reliable method for detecting and classifying unusual and environmental sounds in real-world scenarios. The main tasks involve comparing time–frequency features, integrating advanced deep learning frameworks, and evaluating their performance in industrial, urban, and natural soundscapes. The novelty of this work lies in its cross-domain approach using three datasets (MIMII, ESC50, and FSC22) and in the development of hybrid architectures that combine recurrent neural networks (GRU and LSTM) with modern backbones such as EffNet and SWinT. By integrating spatial representations extracted by convolutional or transformer networks with temporal modeling, the proposed models capture sequential dependencies in noisy audio scenes more effectively. The methodology employs perceptually motivated representations (mel-spectrogram, MFCC, and chroma-STFT) and a rigorous 5-fold cross-validation protocol. Experiments conducted on high-performance computing infrastructure ensure reproducibility and robustness. Performance is evaluated using accuracy, F1-score, and AUC. Results show that the combination of LSTM and SWinT, particularly with mel-spectrogram features, achieves the best performance, reaching 98.9% on MIMII, 90.8% on ESC50, and 82.8% on FSC22.