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The Computational Attitude in Music Theory
Music studies’s turn to computation during the twentieth century has engendered particular habits of thought about music, habits that remain in operation long after the music scholar has stepped away from the computer. The computational attitude is a way of thinking about music that is learned at the computer but can be applied away from it. It may be manifest in actual computer use, or in invocations of computationalism, a theory of mind whose influence on twentieth-century music theory is palpable. It may also be manifest in more informal discussions about music, which make liberal use of computational metaphors. In Chapter 1, I describe this attitude, the stakes for considering the computer as one of its instruments, and the kinds of historical sources and methodologies we might draw on to chart its ascendance. The remainder of this dissertation considers distinct and varied cases from the mid-twentieth century in which computers or computationalist musical ideas were used to pursue new musical objects, to quantify and classify musical scores as data, and to instantiate a generally music-structuralist mode of analysis.
I present an account of the decades-long effort to prepare an exhaustive and accurate catalog of the all-interval twelve-tone series (Chapter 2). This problem was first posed in the 1920s but was not solved until 1959, when the composer Hanns Jelinek collaborated with the computer engineer Heinz Zemanek to jointly develop and run a computer program. Recognizing the transformation wrought on modern statistics and communications technology by information theory, I revisit Abraham Moles’s book Information Theory and Esthetic Perception (orig. 1958) and use its vocabulary to contextualize contemporary information-theoretic work on music that various evokes the computational mind by John. R. Pierce and Mary Shannon, Wilhelm Fucks, and Henry Quastler (Chapter 3). I conclude with a detailed look into a score-segmentation algorithm of the influential American music theorist Allen Forte (Chapter 4). Forte was a skilled programmer who spent several years at MIT in the 1960s, with cutting-edge computers and the company of first-rank figures in the nascent fields of computer science and artificial intelligence. Each one of the researchers whose work is treated in these case studies—at some stage in their relationship with music—adopted what I call the computational attitude to music, to varying degrees and for diverse ends. Of the many questions this dissertation seeks to answer: what was gained by adopting such an attitude? What was lost? Having understood these past explorations of the computational attitude to music, we are better suited ask of ourselves the same questions today
Evidence for Shared Cognitive Processing of Pitch in Music and Language
Language and music epitomize the complex representational and computational capacities of the human mind. Strikingly similar in their structural and expressive features, a longstanding question is whether the perceptual and cognitive mechanisms underlying these abilities are shared or distinct – either from each other or from other mental processes. One prominent feature shared between language and music is signal encoding using pitch, conveying pragmatics and semantics in language and melody in music. We investigated how pitch processing is shared between language and music by measuring consistency in individual differences in pitch perception across language, music, and three control conditions intended to assess basic sensory and domain-general cognitive processes. Individuals’ pitch perception abilities in language and music were most strongly related, even after accounting for performance in all control conditions. These results provide behavioral evidence, based on patterns of individual differences, that is consistent with the hypothesis that cognitive mechanisms for pitch processing may be shared between language and music.National Science Foundation (U.S.). Graduate Research Fellowship ProgramEunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (Grant 5K99HD057522
As contribuições da ciência cognitiva à composição musical
This dissertation’s goal is to construct a detailed map of the principal branches of
cognitive science and their methodological and epistemological contributions to the study
music composition. We are concerned, firstly, with the contributions to the compositional
techniques, and secondly, with their perception. The first chapter deals with the cognitivist
paradigm by means of artificial intelligence. In the second chapter we relate the artificial
intelligence with the music composition, investigating the cognitvist models of composition
by the analysis of automatic compositional systems. The third chapter brings the artificial
neural networks to the scene, within the so-called connectionist paradigm. In our fourth
chapter we established the relation between the connectionism and music composition. In this
sense, we describe implementations that model and/or simulate aspects of perception and
composition. The fifth chapter leaves the computational perspective in the study of cognition
and present alternative proposals in this sense, related to the music composition and
musicology, as the ecological approach to auditory perception and the theories of
emergentism applied to music
A collaborative filtering method for music recommendation
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThe present dissertation focuses on proposing and describing a collaborative filtering approach for
Music Recommender Systems. Music Recommender Systems, which are part of a broader class of
Recommender Systems, refer to the task of automatically filtering data to predict the songs that are
more likely to match a particular profile.
So far, academic researchers have proposed a variety of machine learning approaches for determining
which tracks to recommend to users. The most sophisticated among them consist, often, on complex
learning techniques which can also require considerable computational resources. However, recent
research studies proved that more simplistic approaches based on nearest neighbors could lead to
good results, often at much lower computational costs, representing a viable alternative solution to
the Music Recommender System problem.
Throughout this thesis, we conduct offline experiments on a freely-available collection of listening
histories from real users, each one containing several different music tracks. We extract a subset of 10
000 songs to assess the performance of the proposed system, comparing it with a Popularity-based
model approach. Furthermore, we provide a conceptual overview of the recommendation problem,
describing the state-of-the-art methods, and presenting its current challenges. Finally, the last section
is dedicated to summarizing the essential conclusions and presenting possible future improvements
The Interpersonal Entrainment in Music Performance Data Collection
The Interpersonal Entrainment in Music Performance Data Collection (IEMPDC) comprises six related corpora of music research materials: Cuban Son & Salsa (CSS), European String Quartet (ESQ), Malian Jembe (MJ), North Indian Raga (NIR), Tunisian Stambeli (TS), and Uruguayan Candombe (UC). The core data for each corpus comprises media files and computationally extracted event onset timing data. Annotation of metrical structure and code used in the preparation of the collection is also shared. The collection is unprecedented in size and level of detail and represents a significant new resource for empirical and computational research in music. In this article we introduce the main features of the data collection and the methods used in its preparation. Details of technical validation procedures and notes on data visualization are available as Appendices. We also contextualize the collection in relation to developments in Open Science and Open Data, discussing important distinctions between the two related concepts
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Spring School on Language, Music, and Cognition: Organizing Events in Time
The interdisciplinary spring school “Language, music, and cognition: Organizing events in time” was held from February 26 to March 2, 2018 at the Institute of Musicology of the University of Cologne. Language, speech, and music as events in time were explored from different perspectives including evolutionary biology, social cognition, developmental psychology, cognitive neuroscience of speech, language, and communication, as well as computational and biological approaches to language and music. There were 10 lectures, 4 workshops, and 1 student poster session.
Overall, the spring school investigated language and music as neurocognitive systems and focused on a mechanistic approach exploring the neural substrates underlying musical, linguistic, social, and emotional processes and behaviors. In particular, researchers approached questions concerning cognitive processes, computational procedures, and neural mechanisms underlying the temporal organization of language and music, mainly from two perspectives: one was concerned with syntax or structural representations of language and music as neurocognitive systems (i.e., an intrapersonal perspective), while the other emphasized social interaction and emotions in their communicative function (i.e., an interpersonal perspective). The spring school not only acted as a platform for knowledge transfer and exchange but also generated a number of important research questions as challenges for future investigations
Music Technology Education and a Plugin-Based Platform as a Tool to Enhance Creativity, Multidisciplinarity, Creative Design, and Collaboration Skills
Music technology is known to have the ability to enhance creativity and creative development among students. A high level of engagement has been shown among students who studied and developed musical projects, and among students who were intellectually involved in the process of meaningful exploration. When students develop a music technology project, they use their software design skills to build and combine different artistic and computational components. Here we present a creative education method for computer science and software engineering students, it uses Muzilator, a plugin-based web platform that enables developers to develop a project as a set of independent web applications (plugins). Students can share their plugins with others or use plugins developed by others. We examined 75 projects of teams of computer science students who participated in a Computer Music course. We studied the characteristics of these projects and Muzilator’s effectiveness as a creative education and collaboration tool. Some of our results show that Muzilator-based projects received higher creativity and multidisciplinarity ratings than did other projects, and that high-risk projects were more creative and artistic than low-risk ones. We also found a gender-dependency: women tended more than men to develop interactive applications, while men tended to choose more theoretic (algorithmic), non-interactive projects. Keywords: educational method, creativity, music education, software design, multidisciplinarity. DOI: 10.7176/JEP/12-11-01 Publication date: April 30th 202
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