Vector quantization and signal compression
Vector quantization and signal compression
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Soft Reconstruction of Speech in the Presence of Noise and Packet Loss
IEEE Transactions on Audio, Speech, and Language Processing
LSP-based multiple-description coding for real-time low bit-rate voice over IP
IEEE Transactions on Multimedia
Multiple description quantization by deterministic annealing
IEEE Transactions on Information Theory
Multiple description coding using pairwise correlating transforms
IEEE Transactions on Image Processing
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Speech coding techniques capable of generating encoded representations which are robust against channel losses play an important role in enabling reliable voice communication over packet networks and mobile wireless systems. In this paper, we investigate the use of multiple description index assignments (MDIAs) for loss-tolerant transmission of line spectral frequency (LSF) coefficients, typically generated by state-of-the-art speech coders. We propose a simulated annealing-based approach for optimizing MDIAs for Markov-model-based decoders which exploit inter-and intraframe correlations in LSF coefficients to reconstruct the quantized LSFs from coded bit streams corrupted by channel losses. Experimental results are presented which compare the performance of a number of novel LSF transmission schemes. These results clearly demonstrate that Markov-model-based decoders, when used in conjunction with optimized MDIA, can yield average spectral distortion much lower than that produced by methods such as interleaving/interpolation, commonly used to combat the packet losses.