Animal vocalisations and natural soundscapes are fascinating objects of study, and contain
valuable evidence about animal behaviours, populations and ecosystems. They are studied …

The remarkable success of deep learning in various domains relies on the availability of
large-scale annotated datasets. However, obtaining annotations is expensive and requires …

We present ImageBind, an approach to learn a joint embedding across six different
modalities-images, text, audio, depth, thermal, and IMU data. We show that all combinations …

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 …

Deep learning models are often trained on distributed, web-scale datasets crawled from the
internet. In this paper, we introduce two new dataset poisoning attacks that intentionally …

Large pretrained (eg," foundation") models exhibit distinct capabilities depending on the
domain of data they are trained on. While these domains are generic, they may only barely …

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

We present Unified-IO 2 a multimodal and multi-skill unified model capable of following
novel instructions. Unified-IO 2 can use text images audio and/or videos as input and can …

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 …

We tackle the problem of generating audio samples conditioned on descriptive text captions.
In this work, we propose AaudioGen, an auto-regressive generative model that generates …