  In this paper, we present a compression approach based on the combination of
low-rank matrix factorization and quantization training, to reduce complexity
for neural network based acoustic event detection (AED) models. Our
experimental results show this combined compression approach is very effective.
For a three-layer long short-term memory (LSTM) based AED model, the original
model size can be reduced to 1% with negligible loss of accuracy. Our approach
enables the feasibility of deploying AED for resource-constraint applications.
