Inverse filtering methods commonly use techniques such as regularization and/or smoothing to reduce artifacts created by the inverse filter. revious studies have shown that these additional techniques can themselves introduce audible artifacts. Furthermore, the “optimal” amount of regularization or smoothing must be chosen by trial and error. This paper introduces some adaptive strategies based on analyzing the incoming audio to improve the subjective performance of various inverse filtering methods. The incoming audio signal is processed in blocks and the spectrum or masking curve can be calculated. One can then use the information from the audio signal to modify the inverse filter to help its performance. The characteristics of the incoming audio signal could also be used to determine if the application of an inverse filter is even necessary. In this paper two approaches are used to help define an inverse filter that is dependent on the incoming audio signal based on a frequency-domain fast-deconvolution method.
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