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Generative Fourier-Based Auto-encoders: Preliminary Results

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

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.

Keywords

  • Auto-encoders
  • Generative modelling
  • Fourier Transform

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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  • DOI: 10.1007/978-3-030-64580-9_2
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Fig. 1.
Fig. 2.

Notes

  1. 1.

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

  2. 2.

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

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Correspondence to Alessandro Zonta .

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Zonta, A., El Hassouni, A., Romero, D.W., Tomczak, J.M. (2020). Generative Fourier-Based Auto-encoders: Preliminary Results. In: , 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

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