Automated audio captioning is a cross-modal translation task that aims to generate natural
language descriptions for given audio clips. This task has received increasing attention with …

Mainstream machine listening models are trained to learn audio concepts under the
paradigm of one class label to many recordings focusing on one task. Learning under such …

In the past decade, convolutional neural networks (CNNs) have been widely adopted as the
main building block for end-to-end audio classification models, which aim to learn a direct …

The ability of artificial intelligence (AI) systems to perceive and comprehend audio signals is
crucial for many applications. Although significant progress has been made in this area …

One of the most amazing functions of the human auditory system is the ability to detect all
kinds of sound events in the environment. With the technologies and hardware advances …

Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A
major challenge is to efficiently learn a representation that approximates the true joint …

Crowdsourcing is a popular tool for collecting large amounts of annotated data, but the
specific format of the strong labels necessary for sound event detection is not easily …

Deep generative models have recently achieved impressive performance in speech and
music synthesis. However, compared to the generation of those domain-specific sounds …

One of the problems with automated audio captioning (AAC) is the indeterminacy in word
selection corresponding to the audio event/scene. Since one acoustic event/scene can be …

Audio classification is an active research area with a wide range of applications. Over the
past decade, convolutional neural networks (CNNs) have been the de-facto standard …