  This study deals with content-based musical playlists generation focused on
Songs and Instrumentals. Automatic playlist generation relies on collaborative
filtering and autotagging algorithms. Autotagging can solve the cold start
issue and popularity bias that are critical in music recommender systems.
However, autotagging remains to be improved and cannot generate satisfying
music playlists. In this paper, we suggest improvements toward better
autotagging-generated playlists compared to state-of-the-art. To assess our
method, we focus on the Song and Instrumental tags. Song and Instrumental are
two objective and opposite tags that are under-studied compared to genres or
moods, which are subjective and multi-modal tags. In this paper, we consider an
industrial real-world musical database that is unevenly distributed between
Songs and Instrumentals and bigger than databases used in previous studies. We
set up three incremental experiments to enhance automatic playlist generation.
Our suggested approach generates an Instrumental playlist with up to three
times less false positives than cutting edge methods. Moreover, we provide a
design of experiment framework to foster research on Songs and Instrumentals.
We give insight on how to improve further the quality of generated playlists
and to extend our methods to other musical tags. Furthermore, we provide the
source code to guarantee reproducible research.
