  Music source separation aims to separate polyphonic music into different
types of sources. Most existing methods focus on enhancing the quality of
separated results by using a larger model structure, rendering them unsuitable
for deployment on edge devices. Moreover, these methods may produce low-quality
output when the input duration is short, making them impractical for real-time
applications. Therefore, the goal of this paper is to enhance a lightweight
model, MMDenstNet, to strike a balance between separation quality and latency
for real-time applications. Different directions of improvement are explored or
proposed in this paper, including complex ideal ratio mask, self-attention,
band-merge-split method, and feature look back. Source-to-distortion ratio,
real-time factor, and optimal latency are employed to evaluate the performance.
To align with our application requirements, the evaluation process in this
paper focuses on the separation performance of the accompaniment part.
Experimental results demonstrate that our improvement achieves low real-time
factor and optimal latency while maintaining acceptable separation quality.
