Large-scale multimodal generative modeling has created milestones in text-to-image and
text-to-video generation. Its application to audio still lags behind for two main reasons: the …

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

In the domain of audio processing, Transfer Learning has facilitated the rise of Self-
Supervised Learning and Zero-Shot Learning techniques. These approaches have led to …

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 …

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 …

Machine Listening, as usually formalized, attempts to perform a task that is, from our
perspective, fundamentally human-performable, and performed by humans. Current …

Audio-Language models jointly learn multimodal text and audio representations that enable
Zero-Shot inference. Models rely on the encoders to create powerful representations of the …

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

Humans can easily understand various audio concepts, but conventional audio
classification models fail due to their inability to predict unseen classes during training. To …

We focus on the task of soundscape mapping, which involves predicting the most probable
sounds that could be perceived at a particular geographic location. We utilise recent state-of …