Name
Abstract | ||
|---|---|---|
Many computational models of music fail to capture essential aspects of the high-level musical structure and context, and this limits their usefulness, particularly for musically informed users. We describe two recent approaches to modelling musical harmony, using a probabilistic and a logic-based framework respectively, which attempt to reduce the gap between computational models and human understanding of music. The first is a chord transcription system which uses a high-level model of musical context in which chord, key, metrical position, bass note, chroma features and repetition structure are integrated in a Bayesian framework, achieving state-of-the-art performance. The second approach uses inductive logic programming to learn logical descriptions of harmonic sequences which characterise particular styles or genres. Each approach brings us one step closer to modelling music in the way it is conceptualised by musicians. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-23126-1_1 | CMMR |
Keywords | Field | DocType |
high-level model,modelling music,recent approach,musical harmony,bayesian framework,musical context,logic-based modelling,high-level musical structure,chord transcription system,logic-based framework,computational model | Inductive logic programming,Musical,Computer science,Speech recognition,Computational model,Artificial intelligence,Probabilistic logic,Chord (music),Musical form,AND gate,Bass note | Conference |
Volume | ISSN | Citations |
6684 | 0302-9743 | 5 |
PageRank | References | Authors |
0.43 | 24 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Dixon | 1 | 1164 | 107.57 |
| Matthias Mauch | 2 | 381 | 26.97 |
| Amélie Anglade | 3 | 31 | 3.00 |