Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/426
Title: Comparison of the Effects of Mel Coefficients and Spectrogram Images via Deep Learning in Emotion Classification
Authors: Demircan, Semiye
Örnek, Humar Kahramanlı
Keywords: Speech Emotion Recognition
Deep Neural Network (Dnn)
Convolutional Neural Network (Cnn)
Deep Learning Algorithm
Mel-Frequency Cepstrum Coefficients (Mfcc)
Neural-Network
Speech
Recognition
Architectures
Publisher: INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC
Abstract: In the present paper, an approach was developed for emotion recognition from speech data using deep learning algorithms, a problem that has gained importance in recent years. Feature extraction manually and feature selection steps were more important in traditional methods for speech emotion recognition. In spite of this, deep learning algorithms were applied to data without any data reduction. The study implemented the triple emotion groups of EmoDB emotion data: Boredom, Neutral, and Sadness-BNS; and Anger, Happiness, and Fear-AHF. Firstly, the spectrogram images resulting from the signal data after preprocessing were classified using AlexNET. Secondly, the results formed from the MelFrequency Cepstrum Coefficients (MFCC) extracted by feature extraction methods to Deep Neural Networks (DNN) were compared. The importance and necessity of using manual feature extraction in deep learning was investigated, which remains a very important part of emotion recognition. The experimental results show that emotion recognition through the implementation of the AlexNet architecture to the spectrogram images was more discriminative than that through the implementation of DNN to manually extracted features.
URI: https://doi.org/10.18280/ts.370107
https://hdl.handle.net/20.500.13091/426
ISSN: 0765-0019
1958-5608
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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