  Sound event detection (SED) is the task of identifying sound events along
with their onset and offset times. A recent, convolutional neural networks
based SED method, proposed the usage of depthwise separable (DWS) and
time-dilated convolutions. DWS and time-dilated convolutions yielded
state-of-the-art results for SED, with considerable small amount of parameters.
In this work we propose the expansion of the time-dilated convolutions, by
conditioning them with jointly learned embeddings of the SED predictions by the
SED classifier. We present a novel algorithm for the conditioning of the
time-dilated convolutions which functions similarly to language modelling, and
enhances the performance of the these convolutions. We employ the freely
available TUT-SED Synthetic dataset, and we assess the performance of our
method using the average per-frame $\text{F}_{1}$ score and average per-frame
error rate, over the 10 experiments. We achieve an increase of 2\% (from 0.63
to 0.65) at the average $\text{F}_{1}$ score (the higher the better) and a
decrease of 3\% (from 0.50 to 0.47) at the error rate (the lower the better).
