  This paper presents a methodology for early detection of audio events from
audio streams. Early detection is the ability to infer an ongoing event during
its initial stage. The proposed system consists of a novel inference step
coupled with dual parallel tailored-loss deep neural networks (DNNs). The DNNs
share a similar architecture except for their loss functions, i.e. weighted
loss and multitask loss, which are designed to efficiently cope with issues
common to audio event detection. The inference step is newly introduced to make
use of the network outputs for recognizing ongoing events. The monotonicity of
the detection function is required for reliable early detection, and will also
be proved. Experiments on the ITC-Irst database show that the proposed system
achieves state-of-the-art detection performance. Furthermore, even partial
events are sufficient to achieve good performance similar to that obtained when
an entire event is observed, enabling early event detection.

Sound;Machine Learning;Audio and Speech Processing