  Automatic target sound extraction (TSE) is a machine learning approach to
mimic the human auditory perception capability of attending to a sound source
of interest from a mixture of sources. It often uses a model conditioned on a
fixed form of target sound clues, such as a sound class label, which limits the
ways in which users can interact with the model to specify the target sounds.
To leverage variable number of clues cross modalities available in the
inference phase, including a video, a sound event class, and a text caption, we
propose a unified transformer-based TSE model architecture, where a multi-clue
attention module integrates all the clues across the modalities. Since there is
no off-the-shelf benchmark to evaluate our proposed approach, we build a
dataset based on public corpora, Audioset and AudioCaps. Experimental results
for seen and unseen target-sound evaluation sets show that our proposed TSE
model can effectively deal with a varying number of clues which improves the
TSE performance and robustness against partially compromised clues.
