Sight and hearing are two senses that play a vital role in human communication and scene
understanding. To mimic human perception ability, audio-visual learning, aimed at …

Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …

Binaural audio plays a significant role in constructing immersive augmented and virtual
realities. As it is expensive to record binaural audio from the real world, synthesizing them …

This open access book provides a comprehensive overview of the state of the art in research
and applications of Foundation Models and is intended for readers familiar with basic …

Existing machine learning research has achieved promising results in monaural audio-
visual separation (MAVS). However, most MAVS methods purely consider what the sound …

Binaural audio is obtained by simulating the biological structure of human ears which plays
an important role in artificial immersive spaces. A promising approach is to utilize mono …

Multi-modal remote sensing (RS) involves the fusion of data from multiple sensors, such as
RGB, Multispectral, Hyperspectral, Light Detection and Ranging, Synthetic Aperture Radar …

Audio deepfake detection (ADD) aims to detect the fake audio generated by text-to-speech
(TTS), and voice conversion (VC), etc., which is an emerging topic. Traditionally we read the …

Audio-visual learning has been a major pillar of multi-modal machine learning, where the
community mostly focused on its $\textit {modality-aligned} $ setting, $\textit {ie} $, the audio …

Visually guided spatial sound generation (VGSSG) is a well-suited multimodal learning
method for dealing with recorded videos. However, existing methods are difficult to be …