With the exponential surge in diverse multimodal data, traditional unimodal retrieval
methods struggle to meet the needs of users seeking access to data across various …

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 …

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

In this work, we explore a scalable way for building a general representation model toward
unlimited modalities. We release ONE-PEACE, a highly extensible model with 4B …

Language-queried audio source separation (LASS) is a new paradigm for computational
auditory scene analysis (CASA). LASS aims to separate a target sound from an audio …

In text-audio retrieval (TAR) tasks, due to the heterogeneity of contents between text and
audio, the semantic information contained in the text is only similar to certain frames within …

Audio-Text retrieval takes a natural language query to retrieve relevant audio files in a
database. Conversely, Text-Audio retrieval takes an audio file as a query to retrieve relevant …

Audio-visual learning has been a major pillar of multi-modal machine learning, where the
community mostly focused on its $\textit {modality-aligned} $ setting, $\textit {ie} $, the audio …

We propose Fast Language-Audio Pre-training (FLAP), a self-supervised approach that
efficiently and effectively learns aligned audio and language representations through …