Fundamentals of speech recognition
Fundamentals of speech recognition
The nature of statistical learning theory
The nature of statistical learning theory
Polyphonic music modeling with random fields
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Functional Harmonic Analysis Using Probabilistic Models
Computer Music Journal
On prediction using variable order Markov models
Journal of Artificial Intelligence Research
Probabilistic melodic harmonization
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Discriminative learning for minimum error classification [patternrecognition]
IEEE Transactions on Signal Processing
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
Learning long-term dependencies with gradient descent is difficult
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Chord progressions are the building blocks from which tonal music is constructed. The choice of a particular representation for chords has a strong impact on statistical modeling of the dependence between chord symbols and the actual sequences of notes in polyphonic music. Melodic prediction is used in this paper as a benchmark task to evaluate the quality of four chord representations using two probabilistic model architectures derived from Input/Output Hidden Markov Models (IOHMMs). Likelihoods and conditional and unconditional prediction error rates are used as complementary measures of the quality of each of the proposed chord representations. We observe empirically that different chord representations are optimal depending on the chosen evaluation metric. Also, representing chords only by their roots appears to be a good compromise in most of the reported experiments.