5 ms·

This is really cool, but the difference between A and B was immediately obvious to me. I couldn't find it – did OP say whether or not respondents were musician

This is really cool, but the difference between A and B was immediately obvious to me. I couldn't find it – did OP say whether or not respondents were musicians or not?

ronack 9y ago
Yes, I'd say the most obvious tell was the strict quantization. If they can transfer some of the timing information it might be more convincing.
- macawfish 9y ago
  I thought the most obvious giveaway was that the melody was not articulated at all by the robot. Aside from that, the dynamics in the robot version were completely absent. I actually didn't think it was even a very good song until I listened to the human playing it, then I kinda liked it.
- dontreact 9y ago
  It might be simple to take the amount that each note is ahead or behind where it would be quantized to and model that parameter exactly the same way that dynamics are modeled.
imalikshake 9y ago
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 9y ago
  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 9y ago
    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 9y ago
      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.

This is really cool, but the difference between A and B was immediately obvious to me. I couldn't find it – did OP say whether or not respondents were musicians or not?

ronack 9y ago

Yes, I'd say the most obvious tell was the strict quantization. If they can transfer some of the timing information it might be more convincing.

macawfish 9y ago
I thought the most obvious giveaway was that the melody was not articulated at all by the robot. Aside from that, the dynamics in the robot version were completely absent. I actually didn't think it was even a very good song until I listened to the human playing it, then I kinda liked it.
dontreact 9y ago
It might be simple to take the amount that each note is ahead or behind where it would be quantized to and model that parameter exactly the same way that dynamics are modeled.

imalikshake 9y ago

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 9y ago
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 9y ago
  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 9y ago
    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.