  This report presents our audio event detection system submitted for Task 2,
"Detection of rare sound events", of DCASE 2017 challenge. The proposed system
is based on convolutional neural networks (CNNs) and deep neural networks
(DNNs) coupled with novel weighted and multi-task loss functions and
state-of-the-art phase-aware signal enhancement. The loss functions are
tailored for audio event detection in audio streams. The weighted loss is
designed to tackle the common issue of imbalanced data in background/foreground
classification while the multi-task loss enables the networks to simultaneously
model the class distribution and the temporal structures of the target events
for recognition. Our proposed systems significantly outperform the challenge
baseline, improving F-score from 72.7% to 90.0% and reducing detection error
rate from 0.53 to 0.18 on average on the development data. On the evaluation
data, our submission obtains an average F1-score of 88.3% and an error rate of
0.22 which are significantly better than those obtained by the DCASE baseline
(i.e. an F1-score of 64.1% and an error rate of 0.64).
