  Universal sound separation (USS) is a task of separating mixtures of
arbitrary sound sources. Typically, universal separation models are trained
from scratch in a supervised manner, using labeled data. Self-supervised
learning (SSL) is an emerging deep learning approach that leverages unlabeled
data to obtain task-agnostic representations, which can benefit many downstream
tasks. In this paper, we propose integrating a self-supervised pre-trained
model, namely the audio masked autoencoder (A-MAE), into a universal sound
separation system to enhance its separation performance. We employ two
strategies to utilize SSL embeddings: freezing or updating the parameters of
A-MAE during fine-tuning. The SSL embeddings are concatenated with the
short-time Fourier transform (STFT) to serve as input features for the
separation model. We evaluate our methods on the AudioSet dataset, and the
experimental results indicate that the proposed methods successfully enhance
the separation performance of a state-of-the-art ResUNet-based USS model.
