Autoencoders are a self-supervised learning system where, during training, the output is an
approximation of the input. Typically, autoencoders have three parts: Encoder (which …

Variational auto-encoders (VAEs) are deep generative latent variable models that can be
used for learning the distribution of complex data. VAEs have been successfully used to …

This study investigates the use of non-linear unsupervised dimensionality reduction
techniques to compress a music dataset into a low-dimensional representation which can be …

This article describes a deep latent variable model of speech power spectrograms and its
application to semi-supervised speech enhancement with a deep speech prior. By …

Multichannel blind audio source separation aims to recover the latent sources from their
multichannel mixtures without supervised information. One state-of-the-art blind audio …

Variational autoencoders (VAEs) are powerful (deep) generative artificial neural networks.
They have been recently used in several papers for speech and audio processing, in …

Automatic speaker verification (ASV) systems are highly vulnerable to presentation attacks,
also called spoofing attacks. Replay is among the simplest attacks to mount—yet difficult to …

This paper presents an unsupervised multichannel method that can separate moving sound
sources based on an amortized variational inference (AVI) of joint separation and …

This paper proposes a novel approach for determined blind source separation (BSS)
assisted by deep neural network (DNN). Determined BSS algorithms, including independent …

A multichannel autoencoder deep learning approach is developed to address the present
intrusion detection systems' detection accuracy and false alarm rate. First, two separate …