Please use this identifier to cite or link to this item:
Title: Emotion Extraction and Recognition from Music
Authors: Zhang, F
Meng, H
Li, M
Keywords: Musical Emotion Recognition (MER);EEG;Random Forest;Music
Issue Date: 2016
Citation: 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 2155 - 2160,(2016)
Abstract: Music makes our life lovely because it can affect our mental states significantly with its emotional information inside. Different people might be affected differently from the same music when they listen the music in different situation and mental states. However, the common emotion information the music can be agreed even from peoples with quite different background and cultures. In this paper, we propose an automatic emotion recognition system for the music by extracting different features from the music and machine learning method learning from common knowledge on emotional state of the trained data. Firstly, two-channel audio signals are processed, and typical audio features are extracted. Then some other features used for EEG signal analysis are also extracted. Finally, these features are combined and the random forest classifier is used for the classification. The proposed method has been tested on a public music dataset and the experimental results demonstrate its efficiency in comparison with the state-of-the-art performance in the same dataset.
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdf645.75 kBUnknownView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.