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 …

We present a framework for learning multimodal representations from unlabeled data using
convolution-free Transformer architectures. Specifically, our Video-Audio-Text Transformer …

The advancement of audio-language (AL) multimodal learning tasks has been significant in
recent years, yet the limited size of existing audio-language datasets poses challenges for …

Audio pattern recognition is an important research topic in the machine learning area, and
includes several tasks such as audio tagging, acoustic scene classification, music …

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 …

The Internet of Audio Things (IoAuT) is an emerging research field positioned at the
intersection of the Internet of Things, sound and music computing, artificial intelligence, and …

Audio tagging is an active research area and has a wide range of applications. Since the
release of AudioSet, great progress has been made in advancing model performance, which …

The objectives of this work are cross-modal text-audio and audio-text retrieval, in which the
goal is to retrieve the audio content from a pool of candidates that best matches a given …

Sound event detection (SED) is a task to detect sound events in an audio recording. One
challenge of the SED task is that many datasets such as the Detection and Classification of …

Audio captioning aims to automatically generate a natural language description of an audio
clip. Most captioning models follow an encoder-decoder architecture, where the decoder …