·
Burgoyne,
J. A., and L. Saul. 2005.
Learning Harmonic
Relationships in Digital Audio with Dirichlet-Based Hidden Markov Models. Proceedings
of the International Symposium on Music Information Retrieval: 438-443.
This paper presents a method based
on Hidden Markov Models to detect harmonies and keys in classical music.
Experiments are done on pieces composed by Mozart
·
Chai,
W., and B. Vercoe. 2001. Folk music
classification using hidden Markov models. Proceedings of the
International Conference on Artificial Intelligence.
This paper presents a method based
on Hidden Markov Models classify geographically folk music pieces. Experiments
are done on pieces form German, Austrian and Irish areas.
·
Durey,
Adriane S., and M. Clements. 2001. Melody Spotting Using Hidden
Markov Models. Proceedings of the International Symposium on Music
Information Retrieval.
This paper presents a method based
on Hidden Markov Models classify geographically folk music pieces. Experiments
are done on pieces form German, Austrian and Irish areas.
·
Lee,
K., and H. Hon. 1989. Speaker-Independent
Phone Recognition Using Hidden Markov Models. IEEE Transactions on
Acoustics. Speech, and Signal Processing 37 (11): 1641-8.
This paper presents a method based
on Hidden Markov Models to do phone recognition. A smoothing method increasing
the algorithm performances is developed.
· Orio, N., and F. Déchelle, F. 2001. Score
Following Using Spectral Analysis and Hidden Markov Models. Proceedings
of the International Computer Music Conference: 125-9.
This paper presents a method based
on Hidden Markov Models to do score following. It merges two common approach of
this problem, and use a complex two-layer HMM to deal with performer’s errors
and signal alteration.
·
Pugin,
L. 2006. Optical
Music Recognition of Early Typographic Prints using Hidden Markov Models. Proceedings
of the International Symposium on Music Information Retrieval: 53-6.
This paper is developing Hidden
Markov Models as solution to optical music recognition. It introduces a new
approach inspired by handwriting and speech recognition, where staves and staff
lines are including in the recognition.
·
Rabiner,
L. R. 1989. A
Tutorial on Hidden Markov-Models and Selected Applications in Speech
Recognition. Proceedings of the IEEE 77 (2): 257-286.
This paper is developing Hidden
Markov Models as solution to recognition problems. The author gives an
extensive explanation about HMM problem resolution and implementation.
Applications in speech recognition are presented.
·
Sheh,
A., and D. P. W. Ellis. 2003. Chord Segmentation and
Recognition Using EM-Trained Hidden Markov Models. Proceedings of the
International Symposium on Music Information Retrieval.
This paper presents a method based
on Hidden Markov Models to segment and recognize chords in popular music.
Experiments are done on early Beatles pieces.
·
Roger
Boyle, “Hidden Markov Models",
This website is a simple tutorial on
Hidden Markov Model theory and basic uses.
·
Warakagoda,
Narada D., “A Hybrid ANN-HMM ASR system with NN based adaptive preprocessing”,
Norges Tekniske Høgskole, http://sitesweb/jedlik.phy.bme.hu/~gerjanos/HMM/hoved.html
This website is a Master Thesis
dealing with a complex implementation of HMM in Audio Speech Recognition. The
first part is dedicated to Hidden Markov Model theory.
All the
references and links in this bibliography generally include at least an
introduction to Hidden Markov Model theory.