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Found 7 papers, 1 papers with code
"All of Me": Mining Users' Attributes from their Public Spotify Playlists
In this work, we investigate the relationship between Spotify users' attributes and their public playlists.
Simple Graph Convolutional Networks
Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago.
Polynomial Graph Convolutional Networks
In this paper, we propose a different strategy, considering a single graph convolution layer that independently exploits neighbouring nodes at different topological distances, generating decoupled representations for each of them.
Audio-Visual Target Speaker Enhancement on Multi-Talker Environment using Event-Driven Cameras
In order to overcome this limitation, we propose the use of event-driven cameras and exploit compression, high temporal resolution and low latency, for low cost and low latency motion feature extraction, going towards online embedded audio-visual speech processing.
An Analysis of Speech Enhancement and Recognition Losses in Limited Resources Multi-talker Single Channel Audio-Visual ASR
In this paper, we analyzed how audio-visual speech enhancement can help to perform the ASR task in a cocktail party scenario.
Face Landmark-based Speaker-Independent Audio-Visual Speech Enhancement in Multi-Talker Environments
In this paper, we address the problem of enhancing the speech of a speaker of interest in a cocktail party scenario when visual information of the speaker of interest is available.
Ranked #1 on
Speech Enhancement
on GRID corpus (mixed-speech)
Pre-training of Recurrent Neural Networks via Linear Autoencoders
We propose a pre-training technique for recurrent neural networks based on linear autoencoder networks for sequences, i. e. linear dynamical systems modelling the target sequences.
