SMERS: Music Emotion Recognition Using Support Vector Regression
Byeong-jun Han
Seungmin Ho
Roger B Dannenberg
Eenjun Hwang
10.1184/R1/6609566.v1
https://kilthub.cmu.edu/articles/journal_contribution/SMERS_Music_Emotion_Recognition_Using_Support_Vector_Regression/6609566
Music emotion plays an important role in music retrieval,
mood detection and other music-related applications.
Many issues for music emotion recognition have been
addressed by different disciplines such as physiology,
psychology, cognitive science and musicology. We
present a support vector regression (SVR) based music
emotion recognition system. The recognition process
consists of three steps: (i) seven distinct features are extracted
from music; (ii) those features are mapped into
eleven emotion categories on Thayer’s two-dimensional
emotion model; (iii) two regression functions are trained
using SVR and then arousal and valence values are predicted.
We have tested our SVR-based emotion classifier
in both Cartesian and polar coordinate system empirically.
The result indicates the SVR classifier in the polar representation
produces satisfactory result which reaches
94.55% accuracy superior to the SVR (in Cartesian) and
other machine learning classification algorithms such as
SVM and GMM.
2002-01-01 00:00:00
computer sciences