  This work aims to advance sound event detection (SED) research by presenting
a new large language model (LLM)-powered dataset namely wild domestic
environment sound event detection (WildDESED). It is crafted as an extension to
the original DESED dataset to reflect diverse acoustic variability and complex
noises in home settings. We leveraged LLMs to generate eight different domestic
scenarios based on target sound categories of the DESED dataset. Then we
enriched the scenarios with a carefully tailored mixture of noises selected
from AudioSet and ensured no overlap with target sound. We consider widely
popular convolutional neural recurrent network to study WildDESED dataset,
which depicts its challenging nature. We then apply curriculum learning by
gradually increasing noise complexity to enhance the model's generalization
capabilities across various noise levels. Our results with this approach show
improvements within the noisy environment, validating the effectiveness on the
WildDESED dataset promoting noise-robust SED advancements.
