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Nonperfect filterbanks used for subband adaptive filtering (SAF) are known to impose limitations on the steady-state performance of such systems. In this paper, we quantify the minimum mean-square error (MMSE) and the accuracy with which the overall SAF system can model an unknown system that it is set to identify. First, in case of MMSE limits, the error is evaluated based on a power spectral density description of aliased signal components, which is accessible via a source model for the subband signals that we derive. Approximations of the MMSE can be embedded in a signal-to-alias ratio (SAR), which is a factor by which the error power can be reduced by adaptive filtering. With simplifications, SAR only depends on the filterbanks. Second, in case of modeling, we link the accuracy of the SAF system to the filterbank mismatch in perfect reconstruction. When using modulated filterbanks, both error limits-MMSE and inaccuracy-can be linked to the prototype. We explicitly derive this for generalized DFT modulated filterbanks and demonstrate the validity of the analytical error limits and their approximations for a number of examples, whereby the analytically predicted limits of error quantities compare favorably with simulation

We present a new error concealment technique for audio transmission over heterogeneous packet switched networks based on time-scale modification of correctly received packets. An appropriate time-scale modification algorithm, WSOLA ("Waveform Similarity OverlapAdd"), is used and its parameters are optimized for scaling short audio segments. Particular attention is paid to the additional delay introduced by the new technique. For subjective listening tests, packet loss is simulated at error rates of 20% and 33% and the new technique is compared to previous proposals by category and component judgment of speech quality. The test results show that typical disturbance components of other techniques can be avoided and overall sound quality is higher.

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