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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Generative Fourier-Based Auto-encoders: Preliminary Results
verfasst von : Alessandro Zonta, Ali El Hassouni, David W. Romero, Jakub M. Tomczak
Erschienen in: Machine Learning, Optimization, and Data Science
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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.