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Books on the topic 'Sonification'

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Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Worrall, David. Sonification Design. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-01497-1.

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2

Hunt, Andy, and Andy Hunt. The sonification handbook. Berlin: Logos Verlag, 2011.

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3

Effenberg, Alfred O. Sonification: Ein akustisches Informationskonzept zur menschlichen Bewegung. Schorndorf: Hofmann, 1996.

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4

Gregory, Kramer, Santa Fe Institute (Santa Fe, N.M.), and International Conference on Auditory Display (1st : 1992 : Santa Fe, N.M.), eds. Auditory display: Sonification, audification, and auditory interfaces. Reading, Mass: Addison-Wesley, 1994.

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5

Baca, Julia A. Application of data sonification for enhanced interpretation of numerical model results. [Vicksburg, Miss: U.S. Army Engineer Waterways Experiment Station, 1995.

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6

Sonifikation: Transfer ins Musikalische: Programmbuch zum Festival Sonifikationen - klingende Datenströme: 27.-29. Oktober 2017, Berliner Gesellschaft für Neue Musik = Sonification: transfer into musical arts : program book for the festival Sonifications - audible data streams. Hofheim: Wolke Verlag, 2017.

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7

Scaletti, Carla. Sonification ≠ Music. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.9.

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Starting from the observation that symbolic language is not the only channel for human communication, this chapter examines ‘data sonification’, a means of understanding, reasoning about, and communicating meaning that extends beyond that which can be conveyed by symbolic language alone. Data sonification is a mapping from data generated by a model, captured in an experiment, or otherwise gathered through observation to one or more parameters of an audio signal or sound synthesis model for the purpose of better understanding, communicating, or reasoning about the original model, experiment, or system. Although data sonification shares techniques and materials with data-driven music, it is in the interests of the practitioners of both music composition and data sonification to maintain a distinction between the two fields.

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8

Minciacchi, Diego, and David Rosenboom, eds. Sonification, Perceptualizing Biological Information. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88963-868-0.

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9

Worrall, David. An Introduction to Data Sonification. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199792030.013.0016.

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10

Worrall, David. Sonification Design: From Data to Intelligible Soundfields. Springer, 2019.

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11

Carlson, Licia. Shared Musical Lives. Oxford University PressNew York, 2022. http://dx.doi.org/10.1093/oso/9780197618356.001.0001.

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Abstract This book makes the case for the epistemological and ethical significance of musical experience. Music can be a source of self-knowledge and self-expression and hence can reveal important dimensions of the self to others. This knowledge—both of self and of others—has a moral force as well. Shared musical experience can transform and establish new modes of being with others, cultivate virtues, and expand the moral imagination. The term sonification provides an organizing principle for the arguments in the book. In a scientific context, sonification is defined as the translation of data or information into nonverbal audible tones. Transposing the concept into a philosophical key, this work defines sonification in two ways: first, sonification is the process by which musical experience reveals dimensions of the self and relationships with others; in a more theoretical sense, philosophical sonification refers to the critical re-examination of philosophical concepts, arguments, and theories in view of what musical experience reveals. These two forms of sonification are explored specifically in the context of disability. Reflecting upon the musical lives of people with cognitive and intellectual disabilities brings their experiences into the foreground, challenges and broadens existing conceptions of disability and music, and provides new ways of thinking about musicking and the philosophies of music and disability.

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12

Carlson, Licia. Shared Musical Lives: Philosophy, Disability, and the Power of Sonification. Oxford University Press, Incorporated, 2022.

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13

Sterne, Jonathan, and Mitchell Akiyama. The Recording That Never Wanted to Be Heard and Other Stories of Sonification. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780195388947.013.0115.

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14

Spectral Analysis and Sonification of Simulation Data Generated in a Frequency Domain Experiment. Storming Media, 2002.

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15

Supper, Alexandra. The Search for the “Killer Application”: Drawing the Boundaries around the Sonification of Scientific Data. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780195388947.013.0064.

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16

(Editor), Gregory Kramer, and INTERNATIONAL CONFERENCE ON AUDITORY DIS (Editor), eds. Auditory Display: Sonification, Audification, and Auditory Interfaces (Proceedings Volume 18, Santa Fe Institute Studies in the Sci). Addison Wesley Publishing Company, 1993.

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17

Fe, N. M. ). Santa Fe Institute (Santa, and N. M. ). International Conference on Auditory Display 1992 (Santa Fe. Auditory Display: Sonification, Audification, and Auditory Interfaces : Proceedings (Santa Fe Institute Studies in the Sciences of Complexity Proceedings). Addison Wesley Publishing Company, 1994.

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18

Erickson, Kristin. Performing Algorithms. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.32.

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The chapter considers algorithmic music as the ‘sonification’ of algorithms, a term coined by Carla Scaletti to describe the mapping of numerically represented relations in some domain to relations in an acoustic domain. The chapter looks at the range of ways this concept has been used by the author in composing her works. The chapter identifies isomorphic relationships between algorithms and collaboration, music, and performance, and extends the boundary of the computer to include systems of people and sound. The definition of music and performance is extended to include process, rules, machines, and execution. Examples discussed include performing a bubble sort, pandemic performances (using principles of complex adaptive systems), Mandelbrot music, and M.T.Brain/Telebrain, which send complex algorithmic instructions to multiple performers in real time.

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19

Spiegel, Laurie. Thoughts on Composing with Algorithms. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.26.

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In this chapter Laurie Spiegel, a pioneer of algorithmic logic in music composition, considers various reasons to use algorithms, including their function as descriptors, generators and adjuncts to creative musical practises. Self-simulation (notably, of decision making processes) is juxtaposed against the sonification of external information and various other uses of algorithms are also described. Human input may be minimal or extensive for the logic used to specify parameters of individual sonic events, variations in global informational entropy, inherent structuring or to achieve variation of material. Spiegel values algorithms particularly to allow her to ‘inhabit the state of flow’ of music by freeing her to focus on selected aspects of composing while handing off other aspects to automated procedures. The chapter includes descriptions of the kinds of uses of algorithmic logic that have contributed to the composition of specific musical works.

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20

I'Anson, Mileece. Form, Chaos, and the Nuance of Beauty. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.33.

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In general, musical forms iterate from axioms of pitches and intervals organized by a set of principles, yet when describing music as ‘desirable’ or ‘effective’, we are pointing not to its form, but to our experience of it. In successful music, the composer moves the listener through a series of emotive states in some sense predetermined by the composer, but that are not reducible to the patterns and principles. The argument in this chapter concerns what makes some music capable of eliciting ‘exaltation’ rather than a routine response. Designing chaos into the musical patterns themselves is proposed as one such route. The author uses the evolution of the stochastic processes underlying her ‘aesthetic sonification’ of natural systems and the vocal variabilities of Emma Kirkby and Amy Winehouse to elucidate her thoughts on how chaos can interact with musical forms. Growth in natural systems, while still engaging with ‘choreographed chaos’ has particularly motivated the author.

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21

Dean, Roger T., and Alex McLean, eds. The Oxford Handbook of Algorithmic Music. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.001.0001.

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Algorithmic music appears to be at a turning point in its history, with many new systems and communities of practice developing together, as vibrant musical culture. This handbook brings together dozens of leading researchers and practitioners in the field, blending technical, artistic, cultural and scientific viewpoints into a whole that considers the making of algorithmic music as a rich, and essentially human activity. The book is organised into four sections, the first grounding the topic in the history, philosophy and psychology of algorithmic music. The second section asks 'what can algorithms in music do?', finding answers in computer science, mathematics, machine learning, bio-inspired computation, manipulation of pattern, computational creativity, and live coding. The third section focuses on the music maker, and the role of algorithms in supporting network music, sonification, music interface design, music in computer games, and spatialisation. The final section opens out to culture at large, and considers algorithmic music in terms of its audience reception, sociology, education, politics and the potential for mass consumption. Perhaps just as importantly, these sections are interleaved with reflective pieces from leading practitioners in the field, allowing us to to grasp the pragmatics of making music with algorithms. Combined, these diverse standpoints provide an absorbing, authoritative survey of research and practice from across the algorithmic music field.

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