Mathematical modeling is applied to the input signal and the extracted model parameters are then used to control an adaptive filter. The output of the adaptive filter is fed back to the modeling unit in order to control the modeling adaptation in time. The adaptive filtering produces a signal carrying the information about characteristics of the input source. Finally, the extracted signal characteristics are enhanced to produce the “characterized” output signal.

The purpose of the general adaptive system is to filter the input signal so that it resembles the desired signal. The error signal is the difference between the desired signal and the output signal. This difference is fed back to the adaptive process algorithm that evaluates the resemblance between the two signals according to a performance criterion and modifies the filter’s frequency response by minimizing the error. Conventional applications of adaptive filtering are interference cancellation, system identification, and active noise cancellation.

At each signal sample point the adaptive filter coefficients in IAF are updated in order to adapt even to the fastest time variation in the signal. In various audio processing applications an assumption for short-time stationarity of approximately 45 ms has been widely used. This is not suitable for the characteristics extraction and therefore the model update must be able to adapt according to the input signal properties that may vary significantly depending on the input material. This is accomplished by controlling the model adaptation by monitoring the adaptive filter output.