Wireless transmission systems for high-quality digital audio signals require a low end-to-end delay and strong robustness against channel distortions. In this work we investigate a Bayesian approach to delayless soft-decision decoding of high-quality audio signals jointly exploiting both implicit redundancy within the audio signal and explicit sample-wise redundancy appended by a channel (block) encoder. Because our approach introduces no algorithmic delay, it can be employed in audio transmission systems that are extremely sensitive to latency like, e. g., wireless digital microphones. Experiments carried out with representative audio signals transmitted over AWGN channels show a significant increase in signal quality.

Applications such as professional wireless digital microphones require a transmission of practically uncoded high-quality audio with ultra-low latency on the one hand and robustness to error-prone channels on the other hand. The delay restrictions, however, prohibit the utilization of efficient block or convolutional channel codes for error protection. The contribution of this work is fourfold: We revise and summarize concisely a Bayesian framework for soft-decision audio decoding and present three novel approaches to (almost) latency-free robust decoding of uncompressed audio. Bit reliability information from the transmission channel is exploited, as well as short-term and long-term residual redundancy within the audio signal, and optionally some explicit redundancy in terms of a sample-individual block code. In all cases we utilize variants of higher-order linear prediction to compute prediction probabilities in three novel ways: Firstly by employing a serial cascade of multiple predictors, secondly by exploiting explicit redundancy in form of parity bits, and thirdly by utilizing an interpolative forward/backward prediction algorithm. The first two presented approaches work fully delayless, while the third one introduces an ultra-low algorithmic delay of just a few samples. The effectiveness of the proposed algorithms is proven in simulations with BPSK and typical digital microphone FSK modulation schemes on AWGN and bursty fading channels.

Short-range wireless audio transmission with high quality on the one hand often encounters error-prone channels, while on the other hand decoding delay plays a critical role in the application. A lot of prior art in audio error concealment is either only intuitively motivated or adds too much delay to the transmission. In this paper we pro pose a framework for transmitted audio error concealment without any algorithmic delay. As a novelty, it strongly follows a Bayesian approach for quantized but uncompressed audio, which can in principle be applied to any type of wireless channel yielding some kind of reliability information. Two methods to compute audio prediction coefficients are presented, one based on the autocorrelation method, the other one on the normalized least-mean-square (NLMS) algorithm. Simulation results for additive white Gaussian noise (AWGN) channels show the significant effect of the proposed approaches.

In the past few years microfilm has gained new research interest as a medium for long-term storage of digital data. This became particularly possible by recent advances in laser film recording technology. In contrast to other optical or magnetic storage media, the microfilm digital channel (MDC) still has been subject to characterization in only a few publications. In this paper we investigate iterative channel codes for the MDC, in particular low-density parity-check and turbo convolutional codes. Simulation results show that practically error-free storage can be achieved with code rates even above 0.85 on a monochrome MDC with binary amplitude-shift keying modulation.