An improved vector quantization method using deep neural network

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• 作者：Wenbin Jiang^a ; ^{jwb361@sjtu.edu.cn} ; Peilin Liu^a ; ^{liupeilin@sjtu.edu.cn} ; Fei Wen^a ; ^b ; ^{wenfei@sjtu.edu.cn}
• 关键词：Deep neural network ; Vector quantization ; Auto-encoder ; Binary coding
• 刊名：AEU - International Journal of Electronics and Communications
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：72
• 期：Complete
• 页码：178-183
• 全文大小：708 K
• 卷排序：72

文摘

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).