Authors:
Luka Kraljević
;
Mladen Russo
;
Mia Mlikota
and
Matko Šarić
Affiliation:
University of Split, FESB and Laboratory for Smart Environment Technologies, Croatia
Keyword(s):
Music, Emotion Detection, Cochlea, Gammatone Filterbank.
Related
Ontology
Subjects/Areas/Topics:
Human-Machine Interface
;
Multimedia
;
Multimedia Signal Processing
;
Multimedia Systems and Applications
;
Perceptual/Human Audiovisual System Modeling
;
Telecommunications
Abstract:
Listening to music often evokes strong emotions. With the rapid growth of easily-accessible digital music libraries there is an increasing need in reliable music emotion recognition systems. Common musical features like tempo, mode, pitch, clarity, etc. which can be easily calculated from audio signal are associated with particular emotions and are often used in emotion detection systems. Based on the idea that humans don’t detect emotions from pure audio signal but from a signal that had been previously processed by the cochlea, in this work we propose new cochlear based features for music emotion recognition. Features are calculated from the gammatone filterbank model output and emotion classification is then performed using Support Vector Machine (SVM) and TreeBagger classifiers. Proposed features are evaluated on publicly available 1000 songs database and compared to other commonly used features. Results show that our approach is effective and outperforms other commonly used feat
ures. In the combined features set we achieved accuracy of 83.88% and 75.12% for arousal and valence.
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