Abstract
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 processing, with its diverse signal
representations and a wide range of sources--from human voices to musical
instruments and environmental sounds--poses challenges distinct from those
found in traditional Natural Language Processing scenarios. Nevertheless,
\textit{Large Audio Models}, epitomized by transformer-based architectures,
have shown marked efficacy in this sphere. By leveraging massive amount of
data, these models have demonstrated prowess in a variety of audio tasks,
spanning from Automatic Speech Recognition and Text-To-Speech to Music
Generation, among others. Notably, recently these Foundational Audio Models,
like SeamlessM4T, have started showing abilities to act as universal
translators, supporting multiple speech tasks for up to 100 languages without
any reliance on separate task-specific systems. This paper presents an in-depth
analysis of state-of-the-art methodologies regarding \textit{Foundational Large
Audio Models}, their performance benchmarks, and their applicability to
real-world scenarios. We also highlight current limitations and provide
insights into potential future research directions in the realm of
\textit{Large Audio Models} with the intent to spark further discussion,
thereby fostering innovation in the next generation of audio-processing
systems. Furthermore, to cope with the rapid development in this area, we will
consistently update the relevant repository with relevant recent articles and
their open-source implementations at
https://github.com/EmulationAI/awesome-large-audio-models.