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 threelayer 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.
REF combines quantization and low-rank matrix factorization technique to compress multi-layer recurrent neural network.
Compression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training
ArXiv
ArXiv
Engineering Computer Science