Features Extraction for Music Notes Recognition using Hidden Markov Models

In recent years Hidden Markov Models (HMMs) have been successfully applied to human speech recognition. The present article proves that this technique is also valid to detect musical characteristics, for example: musical notes. However, any recognition system needs to get a suitable set of parameters, that is, a reduced set of magnitudes that represent the outstanding aspects to classify an entity. This paper shows how a suitable parameterisation and adequate HMMs topology make a robust recognition system of musical notes. At the same time, the way to extract parameters can be used in other recognition technologies applied to music.

[1]  Beth Logan,et al.  Mel Frequency Cepstral Coefficients for Music Modeling , 2000, ISMIR.

[2]  Anssi Klapuri,et al.  Melody Description and Extraction in the Context of Music Content Processing , 2003 .

[3]  Barry Vercoe,et al.  Music-listening systems , 2000 .

[4]  Dionisio de Pedro Cursá.  Teoría completa de la música , 1990 .

[5]  Jesús E. Díaz-Verdejo,et al.  Musical Style Recognition by Detection of Compass , 2003, IbPRIA.

[6]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[7]  Mark A. Clements,et al.  Features for melody spotting using hidden Markov models , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  S. Schwerman,et al.  The Physics of Musical Instruments , 1991 .

[9]  L. R. Rabiner,et al.  Recognition of isolated digits using hidden Markov models with continuous mixture densities , 1985, AT&T Technical Journal.

[10]  Adriane Durey,et al.  Melody Spotting Using Hidden Markov Models , 2001, ISMIR.

[11]  Music recognition using note transition context , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).