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    Modeling plate and spring reverberation using a DSP-informed deep neural network 
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    Modeling plate and spring reverberation using a DSP-informed deep neural network

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    Accepted version (1.520Mb)
    Pagination
    ? - ? (5)
    Publisher
    IEEE
    Publisher URL
    https://2020.ieeeicassp.org/
    Metadata
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    Abstract
    Plate and spring reverberators are electromechanical systems first used and researched as means to substitute real room reverberation. Currently, they are often used in music production for aesthetic reasons due to their particular sonic characteristics. The modeling of these audio processors and their perceptual qualities is difficult since they use mechanical elements together with analog electronics resulting in an extremely complex response. Based on digital reverberators that use sparse FIR filters, we propose a signal processing-informed deep learning architecture for the modeling of artificial reverberators. We explore the capabilities of deep neural networks to learn such highly nonlinear electromechanical responses and we perform modeling of plate and spring reverberators. In order to measure the performance of the model, we conduct a perceptual evaluation experiment and we also analyze how the given task is accomplished and what the model is actually learning.
    Authors
    Martinez Ramirez, M; Benetos, E; Reiss, J; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020)
    URI
    https://qmro.qmul.ac.uk/xmlui/handle/123456789/62846
    Collections
    • Electronic Engineering and Computer Science [2314]
    Copyright statements
    © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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