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Found 8 papers, 2 papers with code
emoDARTS: Joint Optimisation of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition
This study presents emoDARTS, a DARTS-optimised joint CNN and Sequential Neural Network (SeqNN: LSTM, RNN) architecture that enhances SER performance.
Ranked #1 on
Speech Emotion Recognition
on MSP-IMPROV
Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models
After the inception of emotion recognition or affective computing, it has increasingly become an active research topic due to its broad applications.
Audio Barlow Twins: Self-Supervised Audio Representation Learning
The Barlow Twins self-supervised learning objective requires neither negative samples or asymmetric learning updates, achieving results on a par with the current state-of-the-art within Computer Vision.
Ranked #1 on
Event Detection
on DCASE 2016 Task 2
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition
Evaluation results show that in a live data feed setting, RL-DA outperforms a baseline strategy by 11% and 14% in cross-corpus and cross-language scenarios, respectively.
Recent Advances in Computer Audition for Diagnosing COVID-19: An Overview
Computer audition (CA) has been demonstrated to be efficient in healthcare domains for speech-affecting disorders (e. g., autism spectrum, depression, or Parkinson's disease) and body sound-affecting abnormalities (e. g., abnormal bowel sounds, heart murmurs, or snore sounds).
Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning
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 number of factors using simpler regression methods.
End2You -- The Imperial Toolkit for Multimodal Profiling by End-to-End Learning
To our knowledge, this is the first toolkit that provides generic end-to-end learning for profiling capabilities in either unimodal or multimodal cases.
Deep Canonical Time Warping
Thus, they fail to capture complex, hierarchical non-linear representations which may prove to be beneficial towards the task of temporal alignment, particularly when dealing with multi-modal data (e. g., aligning visual and acoustic information).
