Tesis sobre el tema "Music and science"

Most people choose to listen to music that they prefer or like such as classical, country or rock. Previous research has focused on how different characteristics of music (i.e., classical versus country) affect the brain. Yet, when listening to preferred music regardless of the type—people report they often experience personal thoughts and memories. To date, understanding how this occurs in the brain has remained elusive. Using network science methods, I evaluated differences in functional brain connectivity when individuals listened to complete songs. Here the results reveal that a circuit important for internally focused thoughts, known as the default mode network, was most connected when listening to preferred music. The results also reveal that listening to a favorite song alters the connectivity between auditory brain areas and the hippocampus, a region responsible for memory and social emotion consolidation. Given that musical preferences are uniquely individualized phenomena and that music can vary in acoustic complexity and the presence or absence of lyrics, the consistency of these results was contrary to previous neuroscientific understanding. These findings may explain why comparable emotional and mental states can be experienced by people listening to music that differs as widely as Beethoven and Eminem. The neurobiological and neurorehabilitation implications of these results are discussed.

Kulitta is a Haskell-based, modular framework for automated composition and machine learning. A central idea to Kulitta's approach is the notion of abstraction: the idea that something can be described at many different levels of detail. Music has many levels of abstraction, ranging from the sound we hear to a paper score and large-scale structural patterns. Music is also very multidimensional and prone to tractability problems. Kulitta works at many of levels of abstraction in stages as a way to mitigate these inherent complexity problems.

Abstract musical structure is generated by using a new category of grammars called probabilistic temporal graph grammars (PTGGs), which are a type of parameterized, context-free grammar that includes variable instantiation, a feature usually only found in grammars for programming languages. This abstract structure can be turned into full music through the use of constraint satisfaction algorithms and equivalence relations based on music theoretic concepts. An extension to an existing algorithm for learning PCFGs provides a way to learn production probabilities for these grammars using corpora of existing music. Kulitta's modules for these features are able to be combined in different ways to support multiple styles of music.

Kulitta's important contributions include (1) algorithms and a generalized Haskell implementation to support PTGGs, (2) additional formalization of existing musical equivalence relations along with a new equivalence relation for modeling jazz harmony, (3) an empirical evaluation strategy for measuring the performance of automated composition algorithms, and (4) the extension of a machine-learning algorithm for PCFGs to support a much broader category of grammars (inclusive of PTGGs) via the use of an oracle. Kulitta's musical performance is also promising, demonstrating both stylistic versatility and aesthetically pleasing results.

Optical Music Recognition (OMR) is a process that automatically converts the image of a music score into symbolic data. OMR can be divided into two main steps: recognition, the goal of which is to recognize “valid” music symbols, and interpretation, to understand the music meaning, such as pitch and rhythm. We focus on the interpretation problem, and more specifically, rhythm interpretation on piano scores.

In this thesis work, we propose a graph-based algorithm, which interprets rhythm by building a rhythm graph on all symbols in a system measure. Our approach represents the notes and rests in a system measure as the vertices of a graph. Then we build the graph by adding voice edges and coincidence edges between pairs of vertices. The graph is constructed under the constraint such that it leads to a meaningful rhythm interpretation. We score the graph based on music notation rules and choose the graph that has the best score. The problem is thus converted into a constrained optimization problem of finding the graph with the highest score. The rhythmic interpretation follows simply from the connected rhythm graph.

To evaluate the graph-based algorithm, we perform an experiment on a dataset specifically built to cover different types of rhythmic challenges encountered in polyphonic piano scores. We conclude that our algorithm is capable of applying measure level notation rules and finding the globally optimal interpretation, even in examples with splitting and merging voices as well as missing tuplets.

The International Federation of Library Associations developed its Functional Requirements for Bibliographic Records (FRBR) without performing user testing to ensure that the model would meet the needs of professionals and users. Analyzing user-generated social tags in relation to FRBR entities and attributes will help determine if the layperson describes objects in a manner that conforms to the FRBR conceptual model. Number one songs from the weekly Billboard Hot 100 charts from 1958 through 2013 were randomly sampled, tags associated with the sampled songs were pulled from the last.fm Web site, and tags were analyzed to determine their relation to FRBR entities and attributes. A percentage of tags map to FRBR entities, validating those entities’ place in the model. User tags that do not map to a FRBR entities shed light on additional means for resource access and discovery, as well as potential issues to consider should the FRBR conceptual model be revised in the future.

This thesis argues that in the process through which the music industry works to co-opt and commodify genres of music, the music is (de)politicized to appeal to a larger audience. While technological advances in digital media and the internet would seem to bring a decentralized (even democratized) structure that diverts the costly music distribution system allowing for more artists and labels to compete, the RIAA has acted to prevent these technologies from developing their greatest potential. First, I demonstrate how music is commodified and marketed towards consumers. The second part of this thesis uses hip hop as an example to demonstrate how the music industry co-opts a genre of music to sell to the largest number of consumers and in the process changes the political significance of that genre. Finally, I argue that the RIAAâ s attack on file-sharers in the name of copyright protection is a technique for the â Big Fourâ to stop competition from independent artists and labels.
Master of Arts

This thesis is a discussion and analysis of the piece I presented at Signal Flow, the graduate music festival at Mills College, on March 8th, 2018 called pls don’t(!) silence ur cellphones. This analysis will be punctuated with various theoretical asides meant to shed light on different aspects of the work and to present a clearer view of my own artistic mission. Since one of the hardest parts of doing work similar to mine is finding the right ways to do it, I hope this paper will at least serve as a resource for those with similar goals. In the spirit of open source, I want the tools and the processes to be as transparent as possible in order to encourage other artists and to expand a now relatively small community. My piece uses only the audience’s cellphones as sound sources. I use a centrally-located projected display supplemented by the cellphone displays themselves to choreograph the audience’s movements around the space. The piece aims to encourage interesting, fulfilling interactions for the audience while producing a complex sonic result through these interactions. In doing this, there arose many intersecting (and often extramusical) concerns and issues that I needed to address. This paper then serves largely to examine the failures and successes of this pursuit in the hopes of outlining future directions for the project. For this piece, audience members are invited to log onto a website on their cellphone. This website contains a brief set of instructions as well as a “start” button which, when pressed, activates a Web Audio app which produces sound. Each phone then basically becomes a mobile speaker in a large speaker array composed of the aggregate of all of the audience members’ phones. The interface on the phones is intentionally spare and minimal in order to encourage audience members to keep their focus elsewhere; it merely displays a solid block of color indicating which group the audience member belongs to at different parts of the piece and flashes white briefly when a new instruction is sent out. The main interface which all audience members react to is a projected image which acts as a sort of topographical map of the performance space. The audience is directed through a sequence of different spatial orientations which are accompanied by changes in the sonic material presented on their phones. The main goals of the piece are 1) to quickly and cheaply create an accessible “high-tech” listening experience, 2) to encourage and foster social contact (while problematizing and questioning the role of instruction and suggestion), and 3) to present various compositional ideas which are inherent to the work’s form and sonic affect.

Catalyzed by the invention of magnetic tape recording, audio production has transformed from technical to artistic, and the roles of producer, engineer, composer, and performer have merged for many forms of music. However, while these roles have changed, the way we interact with audio production tools has not and still relies on the conventions established in the 1970s for audio engineers. Users communicate their audio concepts to these complex tools using knobs and sliders that control low-level technical parameters. Musicians currently need technical knowledge of signals in addition to their musical knowledge to make novel music. However, many experienced and casual musicians simply do not have the time or desire to acquire this technical knowledge. While simpler tools (e.g. Apple's GarageBand) exist, they are limiting and frustrating to users.

To support these audio-production novices, we must build audio-production tools with affordances for them. We must identify interactions that enable novices to communicate their audio concepts without requiring technical knowledge and develop systems that can understand these interactions.

This dissertation advances our understanding of this problem by investigating three interaction types which are inspired by how novices communicate audio concepts to other people: language, vocal imitation, and evaluation. Because learning from an individual can be time consuming for a user, much of this dissertation focuses on how we can learn general audio concepts offline using crowdsourcing rather than user-specific audio concepts. This work introduces algorithms, frameworks, and software for learning audio concepts via these interactions and investigates the strengths and weaknesses of both the algorithms and the interaction types. These contributions provide a research foundation for a new generation of audio-production tools.

This problem is not limited to audio production tools. Other media production tools—such as software for graphics, image, and video design and editing—are also controlled by low-level technical parameters which require technical knowledge and experience to use effectively. The contributions in this dissertation to learn mappings from descriptive language and feedback to low-level control parameters may also be adapted for creative production tools in these other mediums. The contributions in this dissertation can unlock the creativity trapped in everyone who has the desire to make music and other media but does not have the technical skills required for today's tools.