We aimed at learning deep emotion features to recognize speech emotion. Two
convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D …

This study aims at learning deep features from different data to recognise speech emotion.
The authors designed a merged convolutional neural network (CNN), which had two …

In recent years, studies on speech signals have increasingly paid attention to emotional
information. The most challenging aspect in speech emotion recognition (SER) is choosing …

Continuous dimensional emotion recognition from audio is a sequential regression problem,
where the goal is to maximize correlation between sequences of regression outputs and …

This paper proposes a novel machine learning approach for the task of on-line continuous-
time music mood regression, ie, low-latency prediction of the time-varying arousal and …

With an optimal network topology and tuning of hyperpa-rameters, artificial neural networks
(ANNs) may be trained to learn a mapping from low level audio features to one or more …

There has been an increasing attention on learning feature representations from the
complex, high-dimensional audio data applied in various music information retrieval (MIR) …

In consideration of the ever-growing available multimedia data, annotating multimedia
content automatically with feeling (s) expected to arise in users is a challenging problem. In …

Facial expression and emotion recognition from thermal infrared images has attracted more
and more attentions in recent years. However, the features adopted in current work are …

We needed to find deep emotional features to identify emotions from audio signals.
Identifying emotions in spontaneous speech is a novel and challenging subject of research …