Doctoral research

Towards my doctoral degree I carried out practice-based doctoral research at the Guildhall School of Music and Drama, supervised by Prof. Julian Anderson and co-supervised by Prof. Paul Newland. The research was funded by the Portuguese Foundation for Science and Technology (FCT – Fundação para a Ciência e Tecnologia) with funds from the POPH (Programa Operacional Potencial Humano) and Fundo Social Europeu (European Union).

Barra de assinaturas Bolsa FCT

 

 

Algorithm and Decision in Musical Composition (2016)

PDF file (click to download)
Attached Algorithms CD-ROM (click to download the ZIP file)

City University London digital publication: City Research Online

Abstract

Through a series of creative projects this doctorate set out to research how computer-assisted composition (CAC) of music affects decision-making in my compositional practice. By reporting on the creative research journey, this doctorate is a contribution towards a better understanding of the implications of CAC by offering new insights into the composing process. It is also a contribution to the composition discipline as new techniques were devised, together with new applications of existing techniques.

Using OpenMusic as the sole programming environment, the manual/machine interface was explored through different balances between manual and algorithmic composition and through aesthetic reflection guiding the composing process. This helped clarify the purpose, adequacy and nature of each method as decisions were constantly being taken towards completing the artistic projects.

The most suitable use of algorithms was as an environment for developing, testing, refining and assessing compositional techniques and the music materials they generate: a kind of musical laboratory. As far as a technique can be described by a set of rules, algorithms can help formulate and refine it. Also capable of incorporating indeterminism, they can act as powerful devices in discovering unforeseen musical implications and results.

Algorithms alone were found to be insufficient to simulate human creative thought because aspects such as (but not limited to) imagination, judgement and personal bias could only, and hypothetically, be properly simulated by the most sophisticated forms of artificial intelligence. Furthermore, important aspects of composition such as instrumentation, articulation and orchestration were not subjected to algorithmic treatment because, not being sufficiently integrated in OpenMusic currently, they would involve a great deal of knowledge to be specified and adapted to computer language (1). These shortcomings of algorithms, therefore, implied varying degrees of manual interventions to be carried out on raw materials coming out of their evaluations. A combination of manual and algorithmic composition was frequently employed so as to properly handle musical aspects such as cadence, discourse, monotony, mechanicalness, surprise, and layering, among others. The following commentary illustrates this varying dialogue between automation and intervention, placing it in the wider context of other explorations at automating aspects of musical composition.


1 The most developed current algorithmic software for orchestration is Orchids (Esling et al. 2014), developed at IRCAM during the same period as this research. “It provides a set of algorithms and features to reconstruct any time-evolving target sound with a combination of acoustic instruments, given a set of psychoacoustic criteria”. It, thus, serves the specific purpose of orchestrating a target sound that is fed into it.