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HMMs offer a more sophisticated signal recognition process than FSAs, but with greater computational space and time complexity [125, 126]. Like electrical engineering signal processing, HMMs usually involve preprocessing that assumes linear system properties or assumes observation is frequency band limited and not time limited, and thereby inherit the time‐frequency uncertainty relations, Gabor limit, and Nyquist sampling relations. FSA methods can be used to recover (or extract) signal features missed by HMM or classical electrical engineering signal processing. Even if the signal sought is well understood, and a purely HMM‐based approach is possible, this is often needlessly computationally intensive (slow), especially in areas where there is no signal. To address this there are numerous hybrid FSA/HMM approaches (such as BLAST [127] ) that benefit from the O(L) complexity on length L signal with FSA processing, with more targeted processing at O(LN²) complexity with HMM processing (where there are N states in the HMM model).