An adaptive method of extracting at least one of desired electro magnetic wave signals and sound wave signals (40, 42) from a mixture of signals (40, 42, 44, 46) and suppressing noise and interfering signals to produce enhanced signals corresponding to desired signals, said desired signals being predetermined by one or more distinguishing parameter(s), wherein one of said distinguishing parameters is the shape of their statistical probability density functions (pdf); said desired signals' distinguishing parameter(s) differing from the noise and interfering signals' distinguishing parameter(s), said method comprising the steps of: receiving signal data from said desired signals and noise and interfering signals being collected through at least one suitable sensor means for that purpose; sampling said signal data to form discrete-time input signals x i (n); transforming said discrete-time input signals x i (n) into a set of sub-band signals x i (k) (n), said sub-band signals x i (k) (n) being linearly filtered by a predetermined set of sub-band filters (90, 112) producing a predetermined number of output signals y p (k) (n), where each of the output signals y p (k) (n) favour signals with a specific pdf shape; and reconstructing the output signals y p (n) as the enhanced signals with an inverse transformation (100, 114); updating the filter coefficients of said set of sub-band filters (90, 112), h i,n (k,p) (l), for each time-frame of input signals in each sub-band; wherein updating the filter coefficients h i,n (k,p) (l) comprises for every sub-band and for every output, a set of correction terms Δh i,n (k,p) (l) are found such that the norm difference between the linear-filtering of the sub-band input signals and non-linearly transformed intermediate output signals is iteratively minimized; wherein the functions for non-linearly transforming f p (k) (·) depend on the pdf's of the desired signals in a sub-band k, and are chosen such that output samples, that predominantly occupy levels which are expected from desired signals, are passed with higher levels than output samples that predominantly occupy levels which are expected from undesired signals. said desired signals being predetermined by one or more distinguishing parameter(s), wherein one of said distinguishing parameters is the shape of their statistical probability density functions (pdf); said desired signals' distinguishing parameter(s) differing from the noise and interfering signals' distinguishing parameter(s), said method comprising the steps of: receiving signal data from said desired signals and noise and interfering signals being collected through at least one suitable sensor means for that purpose; sampling said signal data to form discrete-time input signals x i (n); transforming said discrete-time input signals x i (n) into a set of sub-band signals x i (k) (n), said sub-band signals x i (k) (n) being linearly filtered by a predetermined set of sub-band filters (90, 112) producing a predetermined number of output signals y p (k) (n), where each of the output signals y p (k) (n) favour signals with a specific pdf shape; and reconstructing the output signals y p (n) as the enhanced signals with an inverse transformation (100, 114); updating the filter coefficients of said set of sub-band filters (90, 112), h i,n (k,p) (l), for each time-frame of input signals in each sub-band; wherein updating the filter coefficients h i,n (k,p) (l) comprises for every sub-band and for every output, a set of correction terms Δh i,n (k,p) (l) are found such that the norm difference between the linear-filtering of the sub-band input signals and non-linearly transformed intermediate output signals is iteratively minimized; wherein the functions for non-linearly transforming f p (k) (·) depend on the pdf's of the desired signals in a sub-band k, and are chosen such that output samples, that predominantly occupy levels which are expected from desired signals, are passed with higher levels than output samples that predominantly occupy levels which are expected from undesired signals.