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A Probabilistic Expert System for Automatic Musical Accompaniment (1999)
| Content Provider | CiteSeerX |
|---|---|
| Author | Raphael, Christopher |
| Abstract | A methodology is presented that allows a computer to play the role of musical accompanist in a non-improvised musical composition for soloist and accompaniment. The modeling of the accompaniment incorporates a number of distinct knowledge sources including timing information extracted in real-time from the soloist's acoustic signal, an understanding of the soloist's interpretation learned from rehearsals, and prior knowledge that guides the accompaniment toward musically plausible renditions. The solo and accompaniment parts are represented collectively as a large number of Gaussian random variables with a specified conditional independence structure --- a Bayesian Belief Network. Within this framework a principled and computationally feasible method for generating real-time accompaniment is presented that incorporates the relevant knowledge sources. The EM algorithm is used to adapt the accompaniment to the soloist's interpretation through a series of rehearsals. A demonstration is provided from J.S. Bach's Cantata 12. |
| File Format | |
| Volume Number | 10 |
| Journal | Journal of Computational and Graphical Statistics |
| Language | English |
| Publisher Date | 1999-01-01 |
| Access Restriction | Open |
| Subject Keyword | Probabilistic Expert System Automatic Musical Accompaniment Non-improvised Musical Composition J.s. Bach Real-time Accompaniment Gaussian Random Variable Acoustic Signal Accompaniment Part Feasible Method Musical Accompanist Specified Conditional Independence Structure Large Number Bayesian Belief Network Em Algorithm Relevant Knowledge Source Distinct Knowledge Source Plausible Rendition |
| Content Type | Text |
| Resource Type | Article |
