An Adaptive, Scalable Packet Loss Recovery Method

We propose a packet loss recovery method that uses an incomplete secondary encoding as redundancy. The recovery is performed by minimum mean squared error estimation. The method adapts to the loss scenario and is rate scalable. It incorporates a statistical model for the quantizers to facilitate real-time adaptation. We apply the method to the encoding of line-spectral frequencies, which are commonly used in speech coding, illustrating the good performance of the method.