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 …

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 is an essential part of our life, but creating it often requires expertise and is time-
consuming. Research communities have made great progress over the past year advancing …

The revolutionary breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) are
extensively being harnessed across a diverse range of domains, eg, forensic science …

Abstract Video-to-audio (V2A) generation leverages visual-only video features to render
plausible sounds that match the scene. Importantly, the generated sound onsets should …

It is an open challenge to obtain high quality training data, especially captions, for text-to-
audio models. Although prior methods have leveraged\textit {text-only language models} to …

We propose Diffusion Inference-Time T-Optimization (DITTO), a general-purpose frame-
work for controlling pre-trained text-to-music diffusion models at inference-time via …

Recently, audio generation tasks have attracted considerable research interests. Precise
temporal controllability is essential to integrate audio generation with real applications. In …

Clustered federated learning (CFL) addresses the performance challenges posed by data
heterogeneity in federated learning (FL) by organizing edge devices with similar data …

We introduce MusicFlow, a cascaded text-to-music generation model based on flow
matching. Based on self-supervised representations to bridge between text descriptions and …