  In this report, we propose three novel methods for developing a sound event
detection (SED) model for the DCASE 2024 Challenge Task 4. First, we propose an
auxiliary decoder attached to the final convolutional block to improve feature
extraction capabilities while reducing dependency on embeddings from
pre-trained large models. The proposed auxiliary decoder operates independently
from the main decoder, enhancing performance of the convolutional block during
the initial training stages by assigning a different weight strategy between
main and auxiliary decoder losses. Next, to address the time interval issue
between the DESED and MAESTRO datasets, we propose maximum probability
aggregation (MPA) during the training step. The proposed MPA method enables the
model's output to be aligned with soft labels of 1 s in the MAESTRO dataset.
Finally, we propose a multi-channel input feature that employs various versions
of logmel and MFCC features to generate time-frequency pattern. The
experimental results demonstrate the efficacy of these proposed methods in a
view of improving SED performance by achieving a balanced enhancement across
different datasets and label types. Ultimately, this approach presents a
significant step forward in developing more robust and flexible SED models
