These are all good suggestions. "Earlier dimensionality reduction" could possibly be used to reduce noise in the eigenvectors: while PCA removes noise as define

These are all good suggestions. "Earlier dimensionality reduction" could possibly be used to reduce noise in the eigenvectors: while PCA removes noise as defined by dimensions with low overall variance, the results still sound noisy from a sound engineering perspective. I saw some research years ago about breaking down sound into pitched and unpitched components, using some kind of kernel transformation in fourier space. If I remember rightly this reduced the usual 'artifacts' you get when manipulating fourier transforms of white noise. Perhaps a good candidate for this case. Sorry I can't remember more though I think the talk was by this guy https://www.york.ac.uk/music/staff/academic/jez-wells/ https://www.york.ac.uk/music/staff/academic/jez-wells/ Either way, to the OP who I presume can only be Dr Brady of the Museum of Techno; good work old chap, you deserve a sherry.