The Problem: Given a Markov analysis module, particularly for percussion, analyzing pieces of different styles (or even a single piece with slight variations in style) and averaging them into a primary statistics file would cause the file to become a "soup" of conflicting styles. This mushy average would turn into a rather nasty output. It's like taking vibrant blue and green, both very nice colors when taken separately, and combining them to get a nasty brown.

Possible Solution: When analyzing pieces, create statistical profiles of each segment (on an individual measure, or maybe a 4-measure basis) and compare the divergence of the statistics. If the divergence measure surpasses a certain threshold (which the user may set), then the segments are treated as separate styles that use separate statistical profiles. If they don't diverge by much, then the statistics can safely be averaged and saved to the main statistical profile for that style. It's like averaging all the shades of red and all the shades of green separately, so as to avoid mixing to get brown. Furthermore, an overarching statistical profile for style transitions could be made so that the drummer knows how often Style A moves to Style B and when, based on the segmented analysis of each piece. In this way, the analysis could conceivable decipher and reproduce an entire sequence of Intro, Verse, Chorus, Verse, etc. without actually understanding what each part means, just knowing that the statistical profiles for each diverge and transition into each other in certain parts of the composition.