  The S{\o}rensen--Dice Coefficient has recently seen rising popularity as a
loss function (also known as Dice loss) due to its robustness in tasks where
the number of negative samples significantly exceeds that of positive samples,
such as semantic segmentation, natural language processing, and sound event
detection. Conventional training of polyphonic sound event detection systems
with binary cross-entropy loss often results in suboptimal detection
performance as the training is often overwhelmed by updates from negative
samples. In this paper, we investigated the effect of the Dice loss, intra- and
inter-modal transfer learning, data augmentation, and recording formats, on the
performance of polyphonic sound event detection systems with multichannel
inputs. Our analysis showed that polyphonic sound event detection systems
trained with Dice loss consistently outperformed those trained with
cross-entropy loss across different training settings and recording formats in
terms of F1 score and error rate. We achieved further performance gains via the
use of transfer learning and an appropriate combination of different data
augmentation techniques.
