  Visual sound source separation aims at identifying sound components from a
given sound mixture with the presence of visual cues. Prior works have
demonstrated impressive results, but with the expense of large multi-stage
architectures and complex data representations (e.g. optical flow
trajectories). In contrast, we study simple yet efficient models for visual
sound separation using only a single video frame. Furthermore, our models are
able to exploit the information of the sound source category in the separation
process. To this end, we propose two models where we assume that i) the
category labels are available at the training time, or ii) we know if the
training sample pairs are from the same or different category. The experiments
with the MUSIC dataset show that our model obtains comparable or better
performance compared to several recent baseline methods. The code is available
at
https://github.com/ly-zhu/Leveraging-Category-Information-for-Single-Frame-Visual-Sound-Source-Separation
