Scopus
YÖKSİS Eşleşti
Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech
Neural Computing and Applications · Nisan 2018
YÖKSİS Kayıtları
Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech
Neural Computing and Applications · 2018 SCI-Expanded
PROFESÖR HUMAR KAHRAMANLI ÖRNEK →
Application of fuzzy C-means clustering algorithm to spectral features for emotion classification from speech
Neural Computing and Applications SCI-Expanded
PROFESÖR HUMAR KAHRAMANLI ÖRNEK →
Makale Bilgileri
DergiNeural Computing and Applications
Yayın TarihiNisan 2018
Cilt / Sayfa29 · 59-66
Scopus ID2-s2.0-84996868118
Özet
In the present study, emotion recognition from speech signals was performed by using the fuzzy C-means algorithm. Spectral features obtained from speech signals were used as features. The spectral features used were Mel frequency cepstral coefficients and linear prediction coefficients. Certain statistical features were extracted from the spectral features obtained in the study. After the selection of the extracted features, cluster centers were identified by using type-1 fuzzy C-means (FCM) algorithm and used as input to the classifier. Supervised classifiers such as ANN, NB, kNN, and SVM were used for classification. In the study, all seven emotions of the EmoDB database were used. Of the features obtained, FCM clustering was applied to Mel coefficients and obtained clusters centers were used as input for classification. The results showed that using FCM for preprocessing aim increased the success rate. The comparison of the classification methods showed that the maximum success rate was obtained as 92.86% using the SVM classifier.
Yazarlar (2)
1
Semiye Demircan
2
Humar Kahramanli
Anahtar Kelimeler
Emotion recognition
Fuzzy C-means
LPC
MFCC
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
46
Atıf
2
Yazar
4
Anahtar Kelime