# Compression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training

arXiv: https://arxiv.org/abs/1905.00855 · HTML: https://mlsanity.com/paper/1905.00855/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2019.

## Built on by (most cited first, 2 of 2)

- [Sub 8-Bit Quantization of Streaming Keyword Spotting Models for Embedded Chipsets](https://mlsanity.com/paper/2207.06920/) (2022)
- [Fixed-point quantization aware training for on-device keyword-spotting](https://mlsanity.com/paper/2303.02284/) (2023)

## Abstract (arXiv)

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.