DocumentCode :
2271167
Title :
Automatic music mood classification via Low-Rank Representation
Author :
Panagakis, Yannis ; Kotropoulos, Constantine
Author_Institution :
Dept. of Inf., Aristotle Univ. of Thessaloniki, Thessaloniki, Greece
fYear :
2011
fDate :
Aug. 29 2011-Sept. 2 2011
Firstpage :
689
Lastpage :
693
Abstract :
The problem of automatic music mood classification is addressed by resorting to low-rank representation of slow auditory spectro-temporal modulations. Recently, it has been shown that if each data class is linearly spanned by a subspace of unknown dimensions and the data are noiseless, the lowest-rank representation (LRR) of a set of test vector samples with respect to a set of training vector samples has the nature of being both dense for within-class affinities and almost zero for between-class affinities. Consequently, the LRR exactly reveals the classification of the data, resulting into the so-called Low-Rank Representation-based Classification (LRRC). The performance of the LRRC is compared against three well-known classifiers, namely the Sparse Representations-based Classifier, Support Vector Machines, and Nearest Neighbor classifiers for music mood classification by conducting experiments on the MTV and the Soundtracks180 datasets. The experimental results validate the effectiveness of the LRRC among the classifiers that is compared to.
Keywords :
music; pattern classification; support vector machines; LRRC; MTV; Soundtracks180 datasets; automatic music mood classification; low-rank representation-based classification; nearest neighbor classifiers; slow auditory spectro-temporal modulations; sparse representations-based classifier; support vector machines; Accuracy; Modulation; Mood; Psychoacoustic models; Support vector machines; Training; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference, 2011 19th European
Conference_Location :
Barcelona
ISSN :
2076-1465
Type :
conf
Filename :
7074168
Link To Document :
بازگشت