A vector space model for automatic indexing
Readings in information retrieval
Automatic Feature Extraction for Classifying Audio Data
Machine Learning
Music genre classification using MIDI and audio features
EURASIP Journal on Applied Signal Processing
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Improving music genre classification using collaborative tagging data
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Integration of text and audio features for genre classification in music information retrieval
ECIR'07 Proceedings of the 29th European conference on IR research
Music playlist generation by assimilating GMMs into SOMs
Pattern Recognition Letters
Feature selection in a cartesian ensemble of feature subspace classifiers for music categorisation
Proceedings of 3rd international workshop on Machine learning and music
Genre classification and the invariance of MFCC features to key and tempo
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Enhanced semantic TV-show representation for personalized electronic program guides
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
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Music genre classification is the categorization of a piece of music into its corresponding categorical labels created by humans and has been traditionally performed through a manual process. Automatic music genre classification, a fundamental problem in the musical information retrieval community, has been gaining more attention with advances in the development of the digital music industry. Most current genre classification methods tend to be based on the extraction of short-time features in combination with high-level audio features to perform genre classification. However, the representation of short-time features, using time windows, in a semantic space has received little attention. This paper proposes a vector space model of mel-frequency cepstral coefficients (MFCCs) that can, in turn, be used by a supervised learning schema for music genre classification. Inspired by explicit semantic analysis of textual documents using term frequency-inverse document frequency (tf-idf), a semantic space model is proposed to represent music samples. The effectiveness of this representation of audio samples is then demonstrated in music genre classification using various machine learning classification algorithms, including support vector machines (SVMs) and k-nearest neighbor clustering. Our preliminary results suggest that the proposed method is comparable to genre classification methods that use low-level audio features.