This survey paper provides a comprehensive overview of the recent advancements and
challenges in applying large language models to the field of audio signal processing. Audio …

Acoustic scene classification (ASC) has gained significant interest recently due to its diverse
applications. Various audio signal processing and machine learning methods have been …

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 …

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 …

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 …

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 …

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 …

The massive growth of self-supervised learning (SSL) has been witnessed in language,
vision, speech, and audio domains over the past few years. While discrete label prediction is …