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 …

We tackle the task of conditional music generation. We introduce MusicGen, a single
Language Model (LM) that operates over several streams of compressed discrete music …

In recent years, foundation models (FMs) such as large language models (LLMs) and latent
diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This …

Speech language models (SpeechLMs) process and generate acoustic data only, without
textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using …

We present SoundStorm, a model for efficient, non-autoregressive audio generation.
SoundStorm receives as input the semantic tokens of AudioLM, and relies on bidirectional …

Text-to-music generation models are now capable of generating high-quality music audio in
broad styles. However, text control is primarily suitable for the manipulation of global musical …

In response to rising concerns surrounding the safety, security, and trustworthiness of
Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red …

While Large Language Models (LLMs) demonstrate impressive capabilities in text
generation, we find that their ability has yet to be generalized to music, humanity's creative …

We introduce MAGNeT, a masked generative sequence modeling method that operates
directly over several streams of audio tokens. Unlike prior work, MAGNeT is comprised of a …

In this work, we define a diffusion-based generative model capable of both music synthesis
and source separation by learning the score of the joint probability density of sources …