Despite the fact that Artificial Intelligence (AI) has boosted the achievement of remarkable
results across numerous data analysis tasks, however, this is typically accompanied by a …

Emotion, a fundamental trait of human beings, plays a pivotal role in shaping aspects of our
lives, including our cognitive and perceptual abilities. Hence, emotion recognition also is …

Providing interpretability of deep-learning models to non-experts, while fundamental for a
responsible real-world usage, is challenging. Attribution maps from xAI techniques, such as …

Current approaches for explaining deep learning systems applied to musical data provide
results in a low-level feature space, eg, by highlighting potentially relevant time-frequency …

The multimedia data has overgrown in the last few years, leading multimodal emotion
analysis to emerging as an important research trend [2]. Research in this direction aims to …

Machine learning is frequently used in affective computing, but presents challenges due the
opacity of state-of-the-art machine learning methods. Because of the impact affective …

Music signals are difficult to interpret from their low-level features, perhaps even more than
images: eg highlighting part of a spectrogram or an image is often insufficient to convey high …

Both predictive uncertainty estimation and visual explanation are crucial elements in helping
humans understand the artificial intelligence (AI) decision-making process and in building …

In today's fast-paced and interconnected world, where human–computer interaction is an
integral component of daily life, the ability to recognize and understand human emotions has …

Speech emotion recognition (SER) is useful in many applications and is approached using
signal processing techniques in the past and deep learning techniques recently. Human …