  This paper introduces effective design choices for text-to-music retrieval
systems. An ideal text-based retrieval system would support various input
queries such as pre-defined tags, unseen tags, and sentence-level descriptions.
In reality, most previous works mainly focused on a single query type (tag or
sentence) which may not generalize to another input type. Hence, we review
recent text-based music retrieval systems using our proposed benchmark in two
main aspects: input text representation and training objectives. Our findings
enable a universal text-to-music retrieval system that achieves comparable
retrieval performances in both tag- and sentence-level inputs. Furthermore, the
proposed multimodal representation generalizes to 9 different downstream music
classification tasks. We present the code and demo online.
