Improving Vector Quantization-Based Decoders for Correlated Processes in Error-Free Transmission
Conference: Speech Communication - 12. ITG-Fachtagung Sprachkommunikation
10/05/2016 - 10/07/2016 at Paderborn, Deutschland
Proceedings: Speech Communication
Pages: 5Language: englishTyp: PDF
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Authors:
Zhao, Ziyue; Han, Sai; Fingscheidt, Tim (Institute for Communications Technology, Technische Universität Braunschweig, Schleinitzstr. 22, 38106 Braunschweig, Germany)
Abstract:
Low bit rate vector quantization (VQ) is omnipresent in today’s media transmission. With IP-based transmission the situation is that source-coded bits are typically either lost/deleted as a whole frame/packet, or they are received correctly. Assuming correctly received VQ symbols we show how to exploit vector-to-vector (i.e., residual temporal) redundancy at the decoder side for an improved reconstruction. It turns out that a feedforward neural network is an effectivemeans for predicting better reconstruction vectors at the receiver in a system-compatible fashion, gaining up to 1 dB SNR depending on signal correlation and bit rate.