An improved vector quantization method using deep neural network

Wenbin Jiang

doi:10.1016/j.aeue.2016.12.002

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An improved vector quantization method using deep neural network

2017-02-01

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Wenbin Jiang *

Peilin Liu

Fei Wen

DOI：10.1016/j.aeue.2016.12.002

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Abstract

En 中文

To address the challenging problem of vector quantization (VQ) for high dimensional vector using large coding bits, this work proposes a novel deep neural network (DNN) based VQ method. This method uses a k-means based vector quantizer as an encoder and a DNN as a decoder. The decoder is initialized by the decoder network of deep auto-encoder, fed with the codes provided by the k-means based vector quantizer, and trained to minimize the coding error of VQ system. Experiments on speech spectrogram coding demonstrate that, compared with the k-means based method and a recently introduced DNN-based method, the proposed method significantly reduces the coding error. Furthermore, in the experiments of coding multi-frame speech spectrogram, the proposed method achieves about 11% relative gain over the k-means based method in terms of segmental signal to noise ratio (SegSNR). (C) 2016 Elsevier GmbH. All rights reserved.

Keywords:

Deep neural network

Vector quantization

Auto-encoder

Binary coding

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