This dissertation presents research in the field ofautomatic music performance with a special focus on piano.
A system is proposed for automatic music performance, basedon artificial neural networks (ANNs). A complex,ecological-predictive ANN was designed thatlistensto the last played note,predictsthe performance of the next note,looksthree notes ahead in the score, and plays thecurrent tone. This system was able to learn a professionalpianist's performance style at the structural micro-level. In alistening test, performances by the ANN were judged clearlybetter than deadpan performances and slightly better thanperformances obtained with generative rules.
The behavior of an ANN was compared with that of a symbolicrule system with respect to musical punctuation at themicro-level. The rule system mostly gave better results, butsome segmentation principles of an expert musician were onlygeneralized by the ANN.
Measurements of professional pianists' performances revealedinteresting properties in the articulation of notes markedstaccatoandlegatoin the score. Performances were recorded on agrand piano connected to a computer.Staccatowas realized by a micropause of about 60% ofthe inter-onset-interval (IOI) whilelegatowas realized by keeping two keys depressedsimultaneously; the relative key overlap time was dependent ofIOI: the larger the IOI, the shorter the relative overlap. Themagnitudes of these effects changed with the pianists' coloringof their performances and with the pitch contour. Theseregularities were modeled in a set of rules for articulation inautomatic piano music performance.
Emotional coloring of performances was realized by means ofmacro-rules implemented in the Director Musices performancesystem. These macro-rules are groups of rules that werecombined such that they reflected previous observations onmusical expression of specific emotions. Six emotions weresimulated. A listening test revealed that listeners were ableto recognize the intended emotional colorings.
In addition, some possible future applications are discussedin the fields of automatic music performance, music education,automatic music analysis, virtual reality and soundsynthesis.
Stockholm: KTH , 2000. , ix, 32 p.
music, performance, expression, interpretation, piano, automatic, artificial neural networks