The Artists who Say Ni!: Incorporating the Python programming language into creative coding for the realisation of musical works

Even though Python is a very popular programming language with a wide range of applications, in the domain of music, specifically electronic music, it is much less used than other languages and programming environments that have been built explicitly for musical creation, such as SuperCollider, Pure Data, Csound, Max, and Chuck. Since 2010 a Python module for DSP called Pyo has been available. This module consists of a complete set of DSP algorithms, Unit Generators, filters, effects, and other tools for the creation of electronic music and sound, yet its community is rather limited. Being part of Python, this module can be combined with a big variety of native and external Python modules for musical or extra-musical tasks, facilitating the realisation of interdisciplinary artworks focusing on music and sound. Starting a creative journey with this module, I was led to more Pythonic techniques for tasks other than music, like mining tweets from Twitter or creating code poetry, which I incorporated into my musical activity. This practice-based research explores the field of the creation of musical works based on Python by focusing on three works. The first one is a live coding poetry opera where the libretto is written in Python. The second one is a live algorithmic composition for an acoustic ensemble based on input from Twitter. The last work is a combination of live coding with live patching on a hardware modular synthesiser system. The main objective of this thesis is to determine the creative potential of Python in music and mixed media art by posing questions that are answered through these works. By doing this, this research aims to provide a conceptual framework for artistic creation that can function as inspiration to other musicians and artists. The title of this thesis is based on one of the most popular lines of the Monty Python comedy troupe, “the Knights who say Ni!”, since the initial developer of the Python programming language, Guido van Rossum, gave this name to this language inspired by Monty Python

Sonifying Urban Rhythms: Towards the spatio-temporal composition of the urban environment

This thesis is concerned with the composition of the urban rhythms generated by urban design and planning. It recognises the temporal limitations of the graphic urban masterplan, with its tendency of being static and singular in the composition of urban experience. Thus it proposes the integration of rhythm into the urban design and planning process, with the aim to improve the temporal quality of urban design. In order to represent these urban rhythms, as designed in the graphic masterplan, we propose their sonification. A Sonified Urban Masterplan (SUM) tool was developed, allowing the sonification of multiple layers of maps (raster or vector images) along a number of paths of interest. An urban sonic code was then developed in order to map the relevant graphic urban parameters into sound parameters. This sonification strategy was applied to the city of Paris as a case study, producing a sonified set of maps whose composition could be ‘listened’ to over time. Temporal issues concerning human movement, transport infrastructure, activity distribution, and the structuring of urban form and design elements could be represented and heard. We then investigated the potential of the SUM tool as a design and planning tool. We explored how sound could be used to inform the composition of urban form in both time and space, in order to generate the urban rhythms we may desire to experience. Thus through the integration of sonification in urban design and planning, this thesis permits the spatio-­‐temporal representation and composition of urban form. It allows urban designers and planners to compose future urban rhythms and improve the temporal quality of our urban environments. Furthermore, the potential of this tool in other fields has also be recognized, for example in music and the composition of multi-­‐layered open graphic scores