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 …

Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general
audio based on text descriptions. However, previous studies in TTA have limited generation …

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 …

Purpose The article discusses the current relevance of artificial intelligence (AI) in research
and how AI improves various research methods. This article focuses on the practical case …

Contrastive learning has shown remarkable success in the field of multimodal
representation learning. In this paper, we propose a pipeline of contrastive language-audio …

This paper studies a simple extension of image-based Masked Autoencoders (MAE) to self-
supervised representation learning from audio spectrograms. Following the Transformer …

Recent advances in transformer-based architectures have shown promise in several
machine learning tasks. In the audio domain, such architectures have been successfully …

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 …

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