87,780 research outputs found

    Evidence for Shared Cognitive Processing of Pitch in Music and Language

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    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

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    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

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    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

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    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

    Music Technology Education and a Plugin-Based Platform as a Tool to Enhance Creativity, Multidisciplinarity, Creative Design, and Collaboration Skills

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    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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