Probabilistic and logic-based modelling of harmony
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
Automatic Chord Estimation from Audio: A Review of the State of the Art
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Genre-Based Music Language Modeling with Latent Hierarchical Pitman-Yor Process Allocation
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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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.