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  1. [SANE STATE NOTES]
  2. Hi, These are some raw unedited notes which I took while watching a video on YouTube.
  3. For some reason, I always feel that I should start sharing what I write. I imagine people reading the words as I write them. Trust me its a weird feeling.
  4.  
  5. The writing process is weird, these are not technically notes..These are my thought when I was watching that. Since there is so much awesomeness in that, I get this weird feeling to write them down because I cannot relive it the same way, based on my memory later.
  6.  
  7. This requires me to pause the video, and get down writing what I was thinking. And then, as I have observed, I wander off and go into unnecessary (and often incorrect) details.
  8.  
  9. Please forgive the typos. Also I have a some more content to share, but I am not sure what use will it serve. Its just an inner feeling to share it.
  10.  
  11. [END]
  12.  
  13. Create virtual stage background.
  14. https://w...content-available-to-author-only...e.com/watch?v=P2x3-b6JEj8
  15.  
  16. Play lights according to the music,  Bass are the blue circles.
  17.  
  18. These have to be either manually operated or pre recorded.
  19.  If this is a  live show how can it be pre recorded ?
  20. This means the base of the songs are pre recorded in live show, the DJ comes and puts some other musical embellishments(Thanks Gavin Harrison).
  21.  
  22.  
  23. I have seen the light operator in Bleed.
  24.  
  25. OR,
  26. the light operates on the basis o, FAILED
  27.  
  28.  
  29. THIS IS BASICALLY A JET AUDIO/SOME OTHER FULL SCREEN EQAULIZER WHICH WOULD COME WITH OLDER MEDAI PLAYERS.
  30. WINAMP ETC.
  31.  
  32. bands  of frequency can be assigned to particular locations on the stage.
  33. Threshold will be kept on indvidiual frequencies in each band. Once threshold is breached on majority of frequencies inside a band, the stage component it is assigned, will light up.
  34.  
  35.  
  36. Can this mapping be automated ?
  37.  
  38.  
  39. I guess it is the same thing.
  40.  
  41. I will need to process the same information required to make the stage lightnings directly.
  42.  
  43. This is basically an extra redundant step, assign bands to different stage components. Its a complex, Animations should be correct etc.
  44.  
  45. So, instead of wasting efforts in converting audio data to an intermediary state, we can use the same calculations to convert it to the stage lighting.
  46.  
  47.  
  48. I hope I makes sense.  (+I -THAT)
  49.  
  50.  
  51.  
  52. BACK
  53.  
  54. Lights are not entirely related to music.
  55. There are some sections which are playing some predefined animations sequences in a loop.
  56. There are sections where the lights come out of this state machine (seamlessly) and starts syncing with the music.
  57.  
  58.  
  59.  
  60.  
  61.  
  62.  
  63. Basically,There will be intervals on the timeline.
  64. Each interval will have a state assigned to them.
  65. If the state is ‘PASSIVE’ this means within that time interval in the song, it will be the passive antimation for that interval.
  66. Each interval is instance  of an animation theme. An animation theme consists of different colours assigned to each stage light.
  67.  
  68. Intervals can be marked “ACTIVE” via a software, like you can select them with a horizontal swipe on your mobile phone.
  69.  
  70. Since we cannot keep a fixed interval size, therefore, In case of automation we’ll need predict that graph,
  71. Intervals can be of varied length. We can’t keep equal length, because we need to the end of each interval to lie on a music transition state.
  72.  
  73. Basically, when a song starts.
  74. At the exact moment there should be a music transition state of PASSIVE TO ACTIVE on the timeline. The end of an PASSIVE intervall should lie here and, the start of the ACTIVE interval should lie next to it.
  75. Imagine, other scenarios, When, the song ends. There should be and ACTIVEPASSIVE arrangements.
  76.  
  77. As can be seen, since song instead of being on mathematical base can still become unpredictable. The mathematical base is like a complex function. IT will create a song.
  78. The mathematical base sets up the lengths of the rhythmic sections of the songs. How long the verse are, how long the chorus, how many lines to so sing  in each verse, are all a defined my mysterious mathematical function.;
  79.  
  80. Its like you are given a random chart.
  81.  
  82.  
  83. When creating an stage visulization, I need to predict, where the PASSTOACTIVE AND ACTIVETOPASS music transition states are.
  84. The chart given will have these states mentioned on the Y AXIS. X axis is the timeline
  85. Since there are only two options, we can assume one is there reference.
  86. Therefore,
  87.  
  88. PASSTOACTIVE means Y = 0
  89. ACTIVETOPASSIVE means Y = 1
  90.  
  91.  
  92. Sorry, I will not erase, there is another state.
  93. There are 3 states, my bad, The NO Transition state.
  94.  
  95.  
  96.  
  97. Therefore,
  98.  
  99. NOCHANGE Y = 0
  100. PASSIVETOACTIVE  Y = 1
  101. ACTIVETO PASSIVE Y = 2
  102.  
  103. Y=[000000000000000000000001000000000000000000000000000000020000000000000000000]
  104.  
  105. X axis is the timeline.
  106.  
  107. In short,since there is function underlying these state changes (plus different songs have different tempos)
  108. We cannot keep a fixed interval size.
  109.  
  110. These examples show because of ignorance while implementation things can get really hard to modify.
  111. The interval object should have The T(Start) and T(End) timestamps.
  112.  
  113. Other calculations should use this instead of using intervalIdX * interval_size as T(Start) and (intervalIdX + 1) * interval_size as T(End)
  114.  
  115. (Interesting analogy: Is beginner programming overfitted programming ? , I think suffers from the same issue as overfitting in machine learning.
  116. Beginner programmers are new, they make programs from the experience they have.
  117. When they are given some task for the first time, they assume that the only case that can happen, is the case mentioned in the problem itself. They overfit ? The overfit on their little data point of 1 programming task.
  118.  
  119. As experience increases, these data points will increase, you will still be overfitting on them, but to an outside, it will look you found the general case. You programs easily handles corner cases. But little do they know it is just because of the sample size. 
  120.  
  121. To re iterate, they fundamental model remains the same. Overfit on your dataset.
  122.  
  123. It is up to you, to expand this dataset.
  124.  
  125. You don’t need to change any thing, the model remains same. We are all instances of the same model the same schema. There model is a scheme if there will only be a single model.
  126.  
  127. You don’t need to change, you just to do. Just keep do whatever you are doing. Just make sure you are moving in the right path and make sure you are moving make sure you are expanding your dataset.
  128.  
  129. JUST OVERFIT
  130.  
  131. I know I maybe contradicting myself here, I do not want to read what did I mean when I was beginning to write this.
  132.  
  133. GOT IT> I STARTED BY SAYING / BLAMING THE BEGINNE PROGRAMMERS TO BE OVERFITTING , BUT I NOW I AM SAYING THAT OTHER SENIOR PROGRAMMERS ALSO OVERFIT>
  134.  
  135.  
  136. SORRY, I THINK THERE ARE SOME  ISSUES.
  137.  
  138. Senior programmers are not overfitting on their data set, this is wrong, I Apologise. I feel bad for people who won’t read this, because most would already leave the blog once they read my lines above about them overfitting. 
  139.  
  140. They, are not overfitting.
  141. They have extra data in the dataset than a beginner programmer -> Agree.
  142. But they also have the power of learning.
  143. Again the model remains same.
  144.  
  145. Earlier model should be removed from production.
  146. Correct Properties:
  147.  Keep increasing data set.
  148.  Use the new data to predict new virtual data in the dataset.
  149.  This data is a slight modification of the original data, since the happening  of this entire plausible We can create a virtual data in the dataset.
  150.  
  151.   When the above is done for each data in dataset, the number of virtual nodes will increase in the dataset.
  152.  
  153.  
  154. AND THEN, THEY WILL OVERFIT
  155.  
  156. YES< THEN THEY WILL OVERFIT. ON THE ORIGINAL DATA AND ON THE VIRTUAL DATA.
  157.  
  158. It will look they know the general case, 
  159.  
  160.  
  161. AGAIN I AM CONTRADICTING MYSELF>
  162.  
  163. NodEND>
  164.  
  165. This video looks fake, people look to good, are they models ? They look in cameras like actors
  166.  
  167.  
  168. What is this feeling called ? When you are sleeping with your eyes closed and for some reason you assume orientation one way (for example laying down with your head on the wrong side of the bed. But intact, when you wake up, you find that to be wrong.
  169. It is weird feeling.
  170.  
  171. In my case, I was thinking I was on the left side of the bed, with the other half of the bed on my left side. I had marked my right side as UNSAFE zone because for me I feel the bed ends there. I had marked my left side as SAFE<Whenever I want to take a turn I can turn to the left side, NO ISSUES.
  172.  
  173. And Then when you wake up / snap out of it, you figure out you were completely wrong.
  174. That is a weird state, 
  175. There is a sense of relief.
  176. That you did not do the wrong thing. You saved yourself by snapping out of it.
  177.  
  178. End  Node
  179.  
  180.  
  181. Rule broken.
  182.  
  183. Do models come there to get noticed ? They pay some dude, buy his ticket, and sit on his shoulders, so that they can get famous from the after movies ?
  184.  
  185. If not, they should.
  186.  
  187.  
  188.  
  189.  
  190.  
  191.  
  192. [PRE PUBLISH FIX: Since we cannot keep a fixed interval size, therefore, In case of automation we’ll need predict that graph,]
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