[imalikshake]

OP here

Thanks for the comment! The respondents weren't musicians. The respondents were selected randomly as I just wanted to see if StyleNet could fool the average person. However, I will definitely be performing surveys on musicians as I continue my work with StyleNet.

[mythas]

First off super cool project with impressive results!! I am not a musician but I was fairly reliably (>75%) able to identify the NN. It took listening to a few examples but I quickly realized that smoothness was the give away. Humans make much more significant jumps when going from soft to aggressive or visa versa where the NN tended to smooth out these changes. I'm going to have my sister (a classically trained pianist) take a listen and see what she thinks.

Edit spelling

[mythas]

So my sister went 14/18. She says,

"The jazz ones are very obvious. Also the voicing of the parts in the first chunk of songs was more nuanced with the human than the AI. The jazz is too straight in the runs with the AI, too "perfect". Real life players stall/hitch, even if just a little! But really, it's pretty impressive - way better than old school canned midi player stuff!"

How effective do you think this approach can be with altering timings to try and imitate that style?

[imalikshake]

Wow, thanks so much for the feedback! It really helps to hear such detailed feedback from a musician.

Learning timing imperfections with my current setup shouldn't be too difficult to implement. Considering that it can predict velocity quite well, I assume that it would be able to pick up timing too. It's definitely the next thing I plan on experimenting with.