These results indicate that the use of VAE feature representations learned using MEG data from multiple individuals can improve the classification accuracy of ...
REPRESENTATION LEARNING BASED ON VARIATIONAL AUTOENCODERS FOR IMAGINED SPEECH CLASSIFICATION ; Session: TU3.PA1: Biomedical Signal Processing and Bioinformatics ...
Dec 23, 2017 · We propose VAEs for deriving the latent representation of speech signals and use this representation to classify emotions.
Dec 15, 2023 · The aim of speech enhancement (SE) [1] is to remove background noise and improve the quality and intelligibility of the observed speech.
Missing: Imagined Classification.
In this thesis, we focus on representations derived through a specific type of generative model, i.e. variational autoencoders (VAEs). VAEs have several ...
Dec 8, 2023 · VAEs are a class of generative models in machine learning that excel in creating new data similar to their training set.
A Two-Stage Deep Representation Learning-Based Speech Enhancement Method Using Variational Autoencoder and Adversarial Training. Article. Jan 2023.
Missing: Imagined Classification.
Variational Autoencoders (VAEs) can be regarded as enhanced Autoencoders where a Bayesian approach is used to learn the probability distribution of the input ...
Learning useful representations with little or no supervision is a key challenge in artificial intelligence. We provide an in-depth review of recent ...
Feb 3, 2024 · They can be used for end-to-end feature extraction and task-specific modelling including image classification, object detection, Natural ...
Missing: Imagined | Show results with:Imagined