I had another question relating to histograms.
I am having a hard time understanding the histogram view. I have read over and over your explanation but still can't get it in my head. After reading your post on the forum about the dynamics riding like techqnique, I thought why not just a waveform. I don't understand the purpose of the 7% on the histogram. Can you please explain the benefit? Also why is the green line so long from the left to where it starts building up activity? That confuses me. Can you also explain the bin? I understand the bin as a container like or a certain area. Is the bin the width of the green city like blocks? I am sorry if I am missing something but I would really like to understand!!!
A histogram shows the frequency (as in how often something occurs) of an event. It comes from probability and statistics theory.
For a pure statistics explanation, lets say I have a bag of ping-pong balls with numbers written on them. The numbers are in the range of 1 to 10. Some of the numbers are the same. Lets say you empty them out and you see these numbers. 1,3,4,5,3,3,1,4,6,7,9,9,9,9,10,7.
A histogram showing the frequency of the numbers from 1 through to 10 is constructed by counting how often each number occurs. So in the above case we have,
That is, there are 2 1's, no 2's, 3 3's, 2 4's, 1 5's and so on and so on.
A normalised histogram usually specifies things in percentages so to convert into a percentage we divide by the total number of balls in the set and multiply by 100. The frequency numbers therefore become,
100 * (2 / 16), 100 * (0 / 16), 100 * (3 / 16), ....
because there are 16 balls in the set. That is what a histogram is. You may note that for normalised histograms the sum of all the frequencies is always 100% because that is the set of all balls in the bag.
Now in the context of Har-Bal's histogram, the balls are each of the sample points on the time line (which are spaced at a nominally 50ms interval) and the number on the balls is the average value for the average histogram and the peak value for the peak histogram. The main difference with the above example is that the time line is a real (ie. fractional) number so how do you group them. Well we group them in "bins" of 1dB width. What does that mean? Take a small subset of average values:
-0.3dB, -0.4dB, -0.7dB, -1.6dB, -2.4dB ....
The bins are centred on exact dB values of 0dB,-1dB,-2dB,-3dB .... The boundary from one bin to the next is the mid-point between centres. The top bin 0dB is special because it doesn't have an upper boundary.
0dB bin ,lower boundary -0.5dB
-1dB bin ,lower boundary -1.5dB, upper boundary -0.5dB
-2dB bin ,lower boundary -2.5dB, upper boundary -1.5dB
-3dB bin ,lower boundary -3.5dB, upper boundary -2.5dB
Going back to the sequence of average values, all we do is figure out which bin the number fits into and when we find that bin we add 1 to it because this corresponds to a count of 1 value fitting in that bin. -0.3dB fits in the 0dB bin, so does -0.4dB, -0.7dB fits into the -1dB bin, -1.6dB fits into the -2dB bin, -2.4dB fits into the -2dB bin and so on and so on. After counting all the values in the bins they get normalised by converting to percentages (100 times the bin count / total number of time line samples).
A histogram, like an average spectrum is much more useful for judging the dynamics of a track because it presents it in a static single image summary. Show me a histogram of the time line and I can tell you immediately whether it is high or low dynamic range, what the limits of the dynamic range is, whether it has a bi-modal behaviour, as in loud parts and quiet parts, whether it has been over limited and so on and so on. It is all there to see in the histogram.
If you find it hard to believe just open up a track you know to have high dynamic range and a track you know to have low dynamic range. Compare the histograms. Can you see the difference. The pattern is obvious and immediately revealing.
The histogram also contains within it the total average level within it. If you give me the histogram I can calculate the track average from the data it presents. It makes a great deal of mathematical sense to summarise dynamics with histograms.