Abstract
The aim of this paper to compare the effect of feature selection methods in emotion recognition from speech and song. Emotion recognition composes of signal processing, feature extraction and classification steps. Nowadays, many studies have focused on common features of speech and song, and have used sub-task classification approach for these systems. In this paper, speech and song data are merged and processed together to focus on the feature selection phase. Autoencoder, Relief-F and Chi-Square selection methods are selected to increase the accuracy of classification. Although selecting features can output similar results, using Relief-F method and Mel Frequency Cepstral Coefficient type of feature outperform these already achieved accuracy rates.
Translated title of the contribution | Comparison of feature selection methods in voice based emotion recognition systems |
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Original language | Turkish |
Title of host publication | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1-4 |
Number of pages | 4 |
ISBN (Electronic) | 9781538615010 |
DOIs | |
Publication status | Published - 5 Jul 2018 |
Event | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 - Izmir, Turkey Duration: 2 May 2018 → 5 May 2018 |
Publication series
Name | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 |
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Conference
Conference | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 |
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Country/Territory | Turkey |
City | Izmir |
Period | 2/05/18 → 5/05/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.