18 citations · 149 across the 26 of their papers we have counts for
11 papers · 1 filter
Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks
Björn W. Schuller, Harry Coppock, Alexander Gaskell
The COVID-19 pandemic has affected the world unevenly; while industrial economies have been able to produce the tests necessary to track the spread of the virus and mostly avoided…
Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning
Zhengxin Joseph Ye, Bjorn W. Schuller
While Post-Earnings-Announcement Drift (PEAD) is one of the most studied stock market anomalies, the current literature is often limited in explaining this phenomenon by a small nu…
High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencoder
Kazi Nazmul Haque, Rajib Rana, Björn W Schuller
Unsupervised disentangled representation learning from the unlabelled audio data, and high fidelity audio generation have become two linchpins in the machine learning research fiel…
A Novel Fusion of Attention and Sequence to Sequence Autoencoders to Predict Sleepiness From Speech
Shahin Amiriparian, Pawel Winokurow, Vincent Karas +3
Motivated by the attention mechanism of the human visual system and recent developments in the field of machine translation, we introduce our attention-based and recurrent sequence…
On Deep Speech Packet Loss Concealment: A Mini-Survey
Mostafa M. Mohamed, Mina A. Nessiem, Björn W. Schuller
Packet-loss is a common problem in data transmission, using Voice over IP. The problem is an old problem, and there has been a variety of classical approaches that were developed t…
ConcealNet: An End-to-end Neural Network for Packet Loss Concealment in Deep Speech Emotion Recognition
Mostafa M. Mohamed, Björn W. Schuller
Packet loss is a common problem in data transmission, including speech data transmission. This may affect a wide range of applications that stream audio data, like streaming applic…