1) Do key detection and pitch-shift all the loops to a common key before processing. That might make more of the melodies come through the eigenvectors.

2) Visualize the loop point cloud - maybe with like 10-50 dimensions of PCA followed by 2 or 3 dimensions T-SNE.

3) Maybe some form of earlier dimensionality reduction? I.e. you could do a short-time fourier transform and then threshold and bin frequencies - then invert the transform to reduce the sounds to their basic characteristics. That make make it so not all of the first 20 or so eigenvectors all have slightly different kick drums. For example, if you have kick drums with fundamentals at 21Hz, 22Hz, 23Hz, 24Hz etc. those will each require two eigenvectors to represent the sine or cosine phases of the signal - but if you could "project" every kick drum sound so they were close to linearly related then PCA could isolate them with fewer eigenvectors.

I would love to play with some sort of live music generation system based on this - really, really interesting ideas. And goes to show what can be built with traditional data analysis techniques and a clever idea!

EDIT: Also if you uploaded your preprocessed data I think that would be really amazing.