A survey of audio-based music classification and annotation
- Authors: Fu, Zhouyu , Lu, Guojun , Ting, Kaiming , Zhang, Dengsheng
- Date: 2011
- Type: Text , Journal article
- Relation: IEEE Transactions on Multimedia Vol. 13, no. 2 (2011), p. 303-319
- Full Text: false
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- Description: Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.
Enhanced polyphonic music genre classification using high level features
- Authors: Arabi, Arash , Lu, Guojun
- Date: 2009
- Type: Text , Conference paper
- Relation: Proceedings of the 2009 IEEE International Conference on Signal and Image Processing Applications p. 1-6
- Full Text: false
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- Description: The task of classifying the genre of polyphonic music signals is traditionally done using only low level features of the signal. In this paper high level features have been applied to improve the task of music genre classification. The use of statistical chord features and chord progression information in conjunction with low level features are proposed in this paper. The chord progression information is manifested in genre probability descriptors calculated using a pattern matching algorithm. Our proposed method provides an improvement of 12.4% in the classification results over a commonly compared technique.
Music emotion annotation by machine learning
- Authors: Cheung, Wai , Lu, Guojun
- Date: 2008
- Type: Text , Conference paper
- Relation: Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing p. 580-585
- Full Text: false
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- Description: Music emotion annotation is a task of attaching emotional terms to musical works. As volume of online musical contents expands rapidly in recent years, demands for retrieval by emotion are emerging. Currently, literature on music retrieval using emotional terms is rare. Emotion annotated data are scarce in existing music databases because annotation is still a manual task. Automating music emotion annotation is an essential prerequisite to research in music retrieval by emotion, for without which even sophisticated retrieval methods may not be very useful in a data deficient environment. This paper describes a machine learning approach to annotate music using a large number of emotional terms. We also estimate the training data size requirements for a workable annotation system. Our empirical result shows that 1) the task of music emotion annotation could be modelled using machine learning techniques to support a large number of emotional terms, 2) the combination of sampling method and data-driven detection threshold is highly effective in optimizing the use of existing annotated data in training machine learning models, 3) synonymous relationships enhance the annotation performance and 4) the training data size requirement is within reach for a workable annotation system. Essentially, automatic music emotion annotation enables music retrieval by emotion to be performed as a text retrieval task.