Theme Transformer: Symbolic Music Generation with Theme-Conditioned Transformer

Model Transformer plugin generation

The current paper presents a new approach using generic and meta-transformations for generating platform-specific transformer plugins from model transformation specifications defined by a combination of graph transformation and abstract state machine rules (as used within the Viatra2 framework). The essence of the approach is to store transformation rules as ordinary models in the model space w...

Theme And Variation Encodings with Roman Numerals (TAVERN): A New Data Set for Symbolic Music Analysis

The Theme And Variation Encodings with Roman Numerals (TAVERN) dataset consists of 27 complete sets of theme and variations for piano composed between 1765 and 1810 by Mozart and Beethoven. In these theme and variation sets, comparable harmonic structures are realized in different ways. This facilitates an evaluation of the effectiveness of automatic analysis algorithms in generalizing across d...

Symbolic Implementation of the Best Transformer

This paper shows how to achieve, under certain conditions, abstract-interpretation algorithms that enjoy the best possible precision for a given abstraction. The key idea is a simple process of successive approximation that makes repeated calls to a theorem prover, and obtains the best abstract value for a set of concrete stores that are represented symbolically, using a logical formula.

Music Mood Classification of Television Theme Tunes

This paper introduces methods used for Music Mood Classification to assist in the automated tagging of television programme theme tunes for the first time. The methods employed use a knowledge driven approach with tailored parameters extractable from the Matlab MIR Toolbox [1]. Four new features were developed, three based on tonality and one on tempo, to enable a degree of quantified tagging, ...

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