11 citations · 39 across the 10 of their papers we have counts for
11 papers
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…
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…
"I have vxxx bxx connexxxn!": Facing Packet Loss in Deep Speech Emotion Recognition
Mostafa M. Mohamed, Björn W. Schuller
In applications that use emotion recognition via speech, frame-loss can be a severe issue given manifold applications, where the audio stream loses some data frames, for a variety…
deepSELF: An Open Source Deep Self End-to-End Learning Framework
Tomoya Koike, Kun Qian, Björn W. Schuller +1
We introduce an open-source toolkit, i.e., the deep Self End-to-end Learning Framework (deepSELF), as a toolkit of deep self end-to-end learning framework for multi-modal signals.…