  A sound event detection (SED) method typically takes as an input a sequence
of audio frames and predicts the activities of sound events in each frame. In
real-life recordings, the sound events exhibit some temporal structure: for
instance, a "car horn" will likely be followed by a "car passing by". While
this temporal structure is widely exploited in sequence prediction tasks (e.g.,
in machine translation), where language models (LM) are exploited, it is not
satisfactorily modeled in SED. In this work we propose a method which allows a
recurrent neural network (RNN) to learn an LM for the SED task. The method
conditions the input of the RNN with the activities of classes at the previous
time step. We evaluate our method using F1 score and error rate (ER) over three
different and publicly available datasets; the TUT-SED Synthetic 2016 and the
TUT Sound Events 2016 and 2017 datasets. The obtained results show an increase
of 9% and 2% at the F1 (higher is better) and a decrease of 7% and 2% at ER
(lower is better) for the TUT Sound Events 2016 and 2017 datasets,
respectively, when using our method. On the contrary, with our method there is
a decrease of 4% at F1 score and an increase of 7% at ER for the TUT-SED
Synthetic 2016 dataset.
