Robust modeling of musical chord sequences using probabilistic N-grams

The modeling of music as a language is a core issue for a wide range of applications such as polyphonic music retrieval, automatic style identification, audio to symbolic music transcription and computer-assisted composition. In this paper, we focus on the modeling of chord sequences by probabilisti...

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Bibliographic Details
Main Authors: Scholz, R., Vincent, E., Bimbot, F.
Format: Conference Proceeding
Language:English
Subjects:
Dictionaries
Hidden Markov models
History
Labeling
model selection
model smoothing
Music
Music information retrieval
N-grams
Natural languages
probabilistic modeling
Robustness
Smoothing methods
Testing
Training data
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Summary:The modeling of music as a language is a core issue for a wide range of applications such as polyphonic music retrieval, automatic style identification, audio to symbolic music transcription and computer-assisted composition. In this paper, we focus on the modeling of chord sequences by probabilistic N-grams. Previous studies using these models have achieved limited success, due to overfitting and to the use of a single chord labeling scheme. We investigate these issues using model smoothing and selection techniques initially designed for spoken language modeling. This approach is evaluated over a set of songs by The Beatles, considering several chord labeling schemes. Initial results show that the accuracy of N-grams is increased but that additional improvements may still be achieved in the future using more advanced, possibly music-specific, smoothing techniques.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2009.4959518