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Generative Fourier-Based Auto-encoders: Preliminary Results

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Machine Learning, Optimization, and Data Science (LOD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12566))

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Abstract

This paper presents a new general framework for turning any auto-encoder into a generative model. Here, we focus on a specific instantiation of the auto-encoder that consists of the Short Time Fourier Transform as an encoder, and a composition of the Griffin-Lim Algorithm and the pseudo inverse of the Short Time Fourier Transform as a decoder. In order to allow sampling from this model, we propose to use the probabilistic Principal Component Analysis. We show preliminary results on the UrbanSound8K Dataset.

AZ is co-financed by the Netherlands Organisation for Applied Scientific Research (TNO). AEH is co-financed by Mobiquity Inc. DWR is co-financed by the Dutch Research Council (NWO) and Semiotic Labs.

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Notes

  1. 1.

    https://github.com/AleZonta/genfae.

  2. 2.

    https://urbansounddataset.weebly.com/urbansound8k.html.

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Author information

Authors and Affiliations

  1. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Alessandro Zonta, Ali El Hassouni, David W. Romero & Jakub M. Tomczak

Authors
  1. Alessandro Zonta
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  2. Ali El Hassouni
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  3. David W. Romero
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  4. Jakub M. Tomczak
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Corresponding author

Correspondence to Alessandro Zonta .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Giorgio Jansen

  5. Almawave, Rome, Italy

    Vincenzo Sciacca

  6. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Zonta, A., El Hassouni, A., Romero, D.W., Tomczak, J.M. (2020). Generative Fourier-Based Auto-encoders: Preliminary Results. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12566. Springer, Cham. https://doi.org/10.1007/978-3-030-64580-9_2

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  • DOI: https://doi.org/10.1007/978-3-030-64580-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64579-3

  • Online ISBN: 978-3-030-64580-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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