Distributed Audio Coding with Efficient Source Correlation Extraction

Distributed source coding is one of the enabling technologies for efficient bandwidth utilization in wireless sensor networks and is consequently of great current interest. This paper studies its application to audio signals, using a transform weighted interleaved vector quantization (TWIN-VQ) framework and allowing a sensor node to passively receive and use information from neighboring sensors that is being transmitted to the common joint decoder. Specifically, it uses the linear predictor coefficients generated as side information by TWIN-VQ for one source to determine its frame-by-frame correlations with another source and then conditionally encode MDCT coefficients of the second source. Based on conditional entropy calculations, exploitation of this correlation results in potential improvements of 54-58% in coding efficiency. Using a multi-context adaptive arithmetic coder, an actual bitrate reduction of 38% is achieved.