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An Intuitive Interface for Digital Synthesizer by Pseudo-intention Learning

Published: 18 September 2019 Publication History
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  • Abstract

    Digital musical instruments are essential technologies in modern musical composition and performance. However, the interface of the synthesizer is not intuitive enough and require extra knowledge because of the parameters. To address this problem, we propose pseudo-intention learning: a novel data collection method for supervised learning in musical instrument development. Pseudo-intention learning collects a data set of the paired target tone and input performed by the user. We developed a conversion framework that reflects the composer's intention by combining standard convolutional neural network and pseudo-intention learning. As a proof of concept, we constructed an interface that can freely manipulate the sound source of a digital snare drum and demonstrated its effectiveness with a pilot study. We confirmed that the tone parameters generated by our system reflected the user's intention. We also discuss applying this method to richer musical expression.

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    cover image ACM Other conferences
    AM '19: Proceedings of the 14th International Audio Mostly Conference: A Journey in Sound
    September 2019
    310 pages
    ISBN:9781450372978
    DOI:10.1145/3356590
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • The University of Nottingham: The University of Nottingham

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    Publication History

    Published: 18 September 2019

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    Author Tags

    1. drum
    2. music
    3. musical instrument
    4. neural networks
    5. supervised learning

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    AM'19
    AM'19: Audio Mostly
    September 18 - 20, 2019
    Nottingham, United Kingdom

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    AM '19 Paper Acceptance Rate 25 of 49 submissions, 51%;
    Overall Acceptance Rate 177 of 275 submissions, 64%

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