Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4954146 | AEU - International Journal of Electronics and Communications | 2017 | 19 Pages |
Abstract
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).
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Networks and Communications
Authors
Wenbin Jiang, Peilin Liu, Fei Wen,