  In recent years, music source separation has been one of the most intensively
studied research areas in music information retrieval. Improvements in deep
learning lead to a big progress in music source separation performance.
However, most of the previous studies are restricted to separating a few
limited number of sources, such as vocals, drums, bass, and other. In this
study, we propose a network for audio query-based music source separation that
can explicitly encode the source information from a query signal regardless of
the number and/or kind of target signals. The proposed method consists of a
Query-net and a Separator: given a query and a mixture, the Query-net encodes
the query into the latent space, and the Separator estimates masks conditioned
by the latent vector, which is then applied to the mixture for separation. The
Separator can also generate masks using the latent vector from the training
samples, allowing separation in the absence of a query. We evaluate our method
on the MUSDB18 dataset, and experimental results show that the proposed method
can separate multiple sources with a single network. In addition, through
further investigation of the latent space we demonstrate that our method can
generate continuous outputs via latent vector interpolation.
