  Sound event localization and detection (SELD) aims to determine the
appearance of sound classes, together with their Direction of Arrival (DOA).
However, current SELD systems can only predict the activities of specific
classes, for example, 13 classes in DCASE challenges. In this paper, we propose
text-queried target sound event localization (SEL), a new paradigm that allows
the user to input the text to describe the sound event, and the SEL model can
predict the location of the related sound event. The proposed task presents a
more user-friendly way for human-computer interaction. We provide a benchmark
study for the proposed task and perform experiments on datasets created by
simulated room impulse response (RIR) and real RIR to validate the
effectiveness of the proposed methods. We hope that our benchmark will inspire
the interest and additional research for text-queried sound source
localization.
