Abstract: This paper aims to develop an acoustic signal-based unsupervised anomaly detection method for automatic machine monitoring. Existing approaches such as deep autoencoder (DAE), variational ...

Abstract: In this paper we present a new implementation of a Variational Autoencoder (VAE) for the calibration of sensors. We propose that the VAE can be used to calibrate sensor data by training the ...

Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...

Disentangled variational autoencoder (D-VAE) separates materials properties from the latent space by conditioning to make inverse materials design more efficient and transparent. It combines labeled ...

This paper is a valuable step in multi-subject behavioral modeling using an extension of the Variational Autoencoder (VAE) framework. Using a novel partition of the latent space and in tandem with a ...

In our recent paper, we propose VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. Several recent end-to-end text-to-speech (TTS) models enabling single ...

Following @Giulero suggestion, I will try to implement a Conditional Variational AutoEncoder to directly connect the input of the aerodynamic dataset (attitude and joint configurations) to the output ...