  Previous approaches in singer identification have used one of monophonic
vocal tracks or mixed tracks containing multiple instruments, leaving a
semantic gap between these two domains of audio. In this paper, we present a
system to learn a joint embedding space of monophonic and mixed tracks for
singing voice. We use a metric learning method, which ensures that tracks from
both domains of the same singer are mapped closer to each other than those of
different singers. We train the system on a large synthetic dataset generated
by music mashup to reflect real-world music recordings. Our approach opens up
new possibilities for cross-domain tasks, e.g., given a monophonic track of a
singer as a query, retrieving mixed tracks sung by the same singer from the
database. Also, it requires no additional vocal enhancement steps such as
source separation. We show the effectiveness of our system for singer
identification and query-by-singer in both the same-domain and cross-domain
tasks.
