Style recognition through statistical event models

Abstract The automatic classification of music fragments into style classes is one challenging problem within the music information retrieval (MIR) domain and also for the understanding of music style perception. This has a number of applications, including the indexation and exploration of musical databases. Some technologies employed in text classification can be applied to this problem. The key point here is to establish the music equivalent to the words in texts. A number of works use the combination of intervals and duration ratios for this purpose. In this paper, different statistical text recognition algorithms are applied to style recognition using this kind of melody representation, exploring their performance for different word sizes.

[1]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[2]  J. Stephen Downie,et al.  Evaluating a simple approach to music information retrieval : conceiving melodic n-grams as text , 1999 .

[3]  Alfons Juan-Císcar,et al.  On the use of Bernoulli mixture models for text classification , 2001, Pattern Recognit..

[4]  Pedro M. Domingos,et al.  Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier , 1996, ICML.

[5]  Gerhard Widmer,et al.  Exploring Music Collections by Browsing Different Views , 2004, Computer Music Journal.

[6]  José Manuel Iñesta Quereda,et al.  Feature-Driven Recognition of Music Styles , 2003, IbPRIA.

[7]  Juan Carlos Pérez-Cortes,et al.  Musical Style Identification Using Grammatical Inference: The Encoding Problem , 2003, CIARP.

[8]  David S. Watson,et al.  A Machine Learning Approach to Musical Style Recognition , 1997, ICMC.

[9]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[10]  Andrew McCallum,et al.  A comparison of event models for naive bayes text classification , 1998, AAAI 1998.

[11]  G. Buzzanca.  A Supervised Learning Approach to Musical Style Recognition , 2002 .

[12]  Barry Vercoe,et al.  Folk Music Classification Using Hidden Markov Models , 2001 .

[13]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[14]  Steve Lawrence,et al.  Artist detection in music with Minnowmatch , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

[15]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[16]  Stefan M. Rüger,et al.  Robust Polyphonic Music Retrieval with N-grams , 2003, Journal of Intelligent Information Systems.

[17]  Hagen Soltau,et al.  Recognition of music types , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[18]  Ichiro Fujinaga,et al.  Automatic Genre Classification Using Large High-Level Musical Feature Sets , 2004, ISMIR.