This is a study of the way in which digital audio and a number of key associated technologies that rely on it as a framework have changed the creation, production and dissemination of music, as witnessed by my own creative practice. The study is built on my own work as an electronic musician and composer and draws from numerous collaborations with not only other musicians but also researchers and artists, as documented through commissions, performances, academic papers and commercial releases over an 9 year period from 2007 to 2016. I begin by contextualising my own musical practice and outlining some prominent themes associated with the democratisation of computing that the work of this thesis interrogates as a critical framework for the production of musical works. I go on to assess how works using various techniques afforded by digital audio may be interpreted as progressively instantiating a digital ontology of music. In the context of this digital ontology of music I propose a method of analysis and criticism of works explicitly concerned with audio analysis and algorithmic processes based on my interpretation of the concept of `hypertext', wherein the ability for computers to analyse, index and create multi-dimensional, non-linear links between segments of digital audio is best described as hypertextual. In light of this, I contextualise the merits of this reading of music created using these affordances of digital audio through a reading of several key works of 20th century music from a hypertextual perspective, emphasising the role information theory and semiotics have to play in analyses of these works. I proffer this as the beginnings of a useful model for musical composition in the domain of digital audio which I seek to explore through my own practice. I then describe and analyse, both individually and in parallel numerous works I have undertaken that seek to interrogate the intricacies of what it means to work in the domain of digital audio with audio analysis, machine listening, algorithmic and generative computational processes and consider the ways in which aspects of this work might be seen as contributing useful and novel insights into music creation by harnessing properties intrinsic to digital audio as a medium. Finally, I emphasise, based on the music and research presented in the thesis, the extent to which digital audio and the harnessing of increasingly complex computational systems for the production and dissemination of music has changed the ontology of music production, a situation which I interpret as creating both substantial challenges, but also great possibilities for the future of music.

En aquesta tesi presentem un mètode general per al reconeixement automàtic d’instruments musicals partint d’un senyal d’àudio. A diferència de molts enfocs relacionats, el nostre evita restriccions artificials o artificioses pel que fa al disseny algorísmic, les dades proporcionades al sistema, o el context d’aplicació. Per tal de fer el problema abordable, limitem el procés a l’operació més bàsica consistent a reconèixer l’instrument predominant en un breu fragment d’àudio. Així ens estalviem la separació de fonts sonores en la mescla i, més específicament, predim una font sonora a partir del timbre general del so analitzat. Per tal de compensar aquesta restricció incorporem, addicionalment, informació derivada d’una anàlisi musical jeràrquica: primer incorporem context temporal a l’hora d’extraure etiquetes dels instruments, després incorporem aspectes formals de la peça que poden ajudar al reconeixement de l’instrument, i finalment incloem informació general gràcies a l’explotació de les associacions entre gèneres musicals i instruments. / In this dissertation we present a method for the automatic recognition of musical instruments from music audio signal. Unlike most related approaches, our specific conception mostly avoids laboratory constraints on the method’s algorithmic design, its input data, or the targeted application context. To account for the complex nature of the input signal, we limit the basic process in the processing chain to the recognition of a single predominant musical instrument from a short audio fragment. We thereby prevent resolving the mixture and rather predict one source from the timbre of the sound. To compensate for this restriction we further incorporate information derived from a hierarchical music analysis; we first incorporate musical context to extract instrumental labels from the time-varying model decisions. Second, the method incorporates information regarding the piece’s formal aspects into the process. Finally, we include information from the collection level by exploiting associations between musical genres and instrumentations.