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      Deep Listening: Early Computational Composition and its Influence on Algorithmic Aesthetics

      RE:SOUND 2019 – 8th International Conference on Media Art, Science, and Technology (RE:SOUND 2019)

      Media Art, Science, and Technology

      August 20-23, 2019

      Machine learning, music composition, aesthetics

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The popularity of the program DeepDream, created by Google engineer Alexander Mordvintsev, exploded due to the open source availability of its uncanny, hallucinogenic aesthetic. Once used to synthesize visual textures, the program popularized the concept of neural network training through image classification algorithms, inspiring visual art interrogating machine learning and the training of proprietary prediction algorithms; though DeepDream has facilitated the production of many mundane examples of surreal computer art, it has also helped to produce some conceptually rich visual investigations, including MacArthur “genius” awardee Trevor Paglen’s recent installation A Study of Invisible Images. While the significance of trained neural networks is presently considered valuable to computer vision experimentation, a medial archaeological investigation of the conceptual underpinnings of machine learning reveals the fundamental influence early sonic experiments in computational music have in its computational and conceptual framework. Early computational music works, such as Lejaren Hiller Jr. and Leonard Isaacson’s Illiac Suite (1957), the first score composed by a computer, as well as Hiller and John Cage’s ambitious multimedia performance HPSCHD (1969), used stochastic models to automate game-like processes, such as Giovanni Pierluigi da Palestrina’s Renaissance-era polyphonic instruction, as well as the I Ching divination process of casting coins or yarrow stalks. Hiller's concerns regarding the historical use of compositional/mathematical gameplay uncovers a conceptual and performative emphasis anticipating the “training” of visual models. Through the adverse reactions of audiences to Hiller’s compositions, written by what the press deemed derogatorily “An electronic brain” in 1957 parallel public reactions to the disturbing mutations of DeepDream, popular participation in the open source project signals a growing willingness to collaborate creatively with computers to interrogate both computational and cognitive processes.

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          Some Studies in Machine Learning Using the Game of Checkers

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                Author and article information

                Contributors
                Conference
                August 2019
                August 2019
                : 43-50
                Affiliations
                University of Illinois at Chicago

                Chicago, Illinois 60607
                Article
                10.14236/ewic/RESOUND19.7
                © Funk. Published by BCS Learning and Development Ltd. Proceedings of RE:SOUND 2019

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                RE:SOUND 2019 – 8th International Conference on Media Art, Science, and Technology
                RE:SOUND 2019
                8
                Aalborg, Denmark
                August 20-23, 2019
                Electronic Workshops in Computing (eWiC)
                Media Art, Science, and Technology
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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