Papers by Muhammet Pakyurek
Müşterilerin GSP analizi kullanarak kümelenmesi
Customer clustering using RFM analysis
Signal Processing and Communications Applications Conference, 2018
Elektronika ir Elektrotechnika
This paper introduces two significant contributions: one is a new feature based on histograms of ... more This paper introduces two significant contributions: one is a new feature based on histograms of MFCC (Mel-Frequency Cepstral Coefficients) extracted from the audio files that can be used in emotion classification from speech signals, and the other – our new multi-lingual and multi-personal speech database, which has three emotions. In this study, Berlin Database (BD) (in German) and our custom PAU database (in English) created from YouTube videos and popular TV shows are employed to train and evaluate the test results. Experimental results show that our proposed features lead to better classification of results than the current state-of-the-art approaches with Support Vector Machine (SVM) from the literature. Thanks to our novel feature, this study can outperform a number of MFCC features and SVM classifier based studies, including recent researches. Due to the lack of our novel feature based approaches, one of the most common MFCC and SVM framework is implemented and one of the mo...
Histogram Based Emotion Recognition from Speech Data
emotion recognition from speech
A Comparative Evaluation of Foreground/Background Segmentation Algorithms
Uploads
Papers by Muhammet Pakyurek