  This work explores domain generalization (DG) for sound event detection
(SED), advancing adaptability towards real-world scenarios. Our approach
employs a mean-teacher framework with domain generalization to integrate
heterogeneous training data, while preserving the SED model performance across
the datasets. Specifically, we first apply mixstyle to the frequency dimension
to adapt the mel-spectrograms from different domains. Next, we use the adaptive
residual normalization method to generalize features across multiple domains by
applying instance normalization in the frequency dimension. Lastly, we use the
sound event bounding boxes method for post-processing. Our approach integrates
features from bidirectional encoder representations from audio transformers and
a convolutional recurrent neural network. We evaluate the proposed approach on
DCASE 2024 Challenge Task 4 dataset, measuring polyphonic SED score (PSDS) on
the DESED dataset and macro-average pAUC on the MAESTRO dataset. The results
indicate that the proposed DG-based method improves both PSDS and macro-average
pAUC compared to the challenge baseline.
