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    A Near-Real Time Automatic Audio Classification: Special Case for Hacivat and Karagoz Shadow Play
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sevdi, Onur Eren; Topaloglu, Yakup; Kayaarma, Selma Yilmazyildiz
    Speaker identification plays a key role in various applications, such as security, biometrics, and human-computer interaction. As a specific task under the domain of audio classification, speaker identification aims to recognize individuals based on their voice characteristics. This paper presents a comparison between three widely adopted neural network architectures and evaluates their performance as classifiers for a real-time speaker identification system. A custom-collected dataset was gathered using publicly shared YouTube videos of a single speaker imitating multiple characters from traditional Turkish shadow play Karagoz and Hacivat. Both MFCC and Log-Mel filterbank energy features were used during the training of CRNN, 2D-CNN and Bi-LSTM architectures. Among these architectures, 2D-CNN achieved the highest accuracy with a value of 94.4% and was approximately 2.7 times faster than its closest follower Bi-LSTM during real-time testing on RTX 4070 Super GPU. © 2025 IEEE.