  Some studies have revealed that contexts of scenes (e.g., "home," "office,"
and "cooking") are advantageous for sound event detection (SED). Mobile devices
and sensing technologies give useful information on scenes for SED without the
use of acoustic signals. However, conventional methods can employ pre-defined
contexts in inference stages but not undefined contexts. This is because
one-hot representations of pre-defined scenes are exploited as prior contexts
for such conventional methods. To alleviate this problem, we propose
scene-informed SED where pre-defined scene-agnostic contexts are available for
more accurate SED. In the proposed method, pre-trained large-scale language
models are utilized, which enables SED models to employ unseen semantic
contexts of scenes in inference stages. Moreover, we investigated the extent to
which the semantic representation of scene contexts is useful for SED.
Experimental results performed with TUT Sound Events 2016/2017 and TUT Acoustic
Scenes 2016/2017 datasets show that the proposed method improves micro and
macro F-scores by 4.34 and 3.13 percentage points compared with conventional
Conformer- and CNN--BiGRU-based SED, respectively.
