Improving Vector Quantization-Based Decoders for Correlated Processes in Error-Free Transmission

Konferenz: Speech Communication - 12. ITG-Fachtagung Sprachkommunikation
05.10.2016 - 07.10.2016 in Paderborn, Deutschland

Tagungsband: ITG-Fb. 267: Speech Communication

Seiten: 5Sprache: EnglischTyp: PDF

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Autoren:
Zhao, Ziyue; Han, Sai; Fingscheidt, Tim (Institute for Communications Technology, Technische Universität Braunschweig, Schleinitzstr. 22, 38106 Braunschweig, Germany)

Inhalt:
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.