Allpass transformed filter banks provide a nonuniform
frequency resolution and can be used in mobile speech
processing systems, e.g., cellular phones or digital hearing aids.
The nominal design of such an allpass transformed analysissynthesis
filter bank (AS FB) with near perfect reconstruction
(NPR) is achieved by numerical optimization of finite-impulse
response (FIR) equalizers in each subchannel. The underlying
nominal optimization problem is an equality constrained leastsquares
problem. In a robust design, we take into account
coefficient uncertainty in a possible implementation of such a
filter bank. We will describe this uncertainty by the choice of two
simple set-based worst-case uncertainty models, namely a norm
bound error model and a coefficient bound error model. When
including these error models, both robust designs can be recast
as second-order cone programs (SOCP) and solved efficiently
by standard numerical optimization methods. Furthermore, we
will provide design examples to show that both robust designs
maintain a good overall performance with respect to NPR while
offering less sensitivity to quantization errors.
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