Mm - hmm.
Um , then I started to optimize the different parameters. And , uh , the first thing I tried to optimize is the , um , time constant of the smoothing. And it seems that the one that I chose for the first experiment was the optimal one , so  uh ,
It 's amazing how often that happens.
Um , so this is the first thing. Um  Yeah , another thing that I  it 's important to mention is , um , that this has a this has some additional latency. Um. Because when I do the smoothing , uh , it 's a recursion that estimated the means , so  of the g of the gain curve. And this is a filter that has some latency. And I noticed that it 's better if we take into account this latency. So , instead o of using the current estimated mean to , uh , subtract the current frame , it 's better to use an estimate that 's some somewhere in the future. Um