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Lossless / Other Codecs / Re: New lossless audio codec in development
Last post by Hakan Abbas -What does it do, really? Sure fixed predictors, then a Levinson-Durbin on who knows how you have (or have not) windowed the data - and then: Does the machine learning start to learn from there on, or from scratch?DLP is completely different from Levinson-Durbin. However, it can be thought of as a dynamic version of fixed estimators. The learning mechanism in DLP is actually trying to find the best case for a block selected from zero (e.g. 512 samples), i.e. the case with the least error. Here a decision is made by looking at only 2 or 3 samples. And the mistakes made are tried to be improved. This is not very interesting.
And are those coefficients at the end really ... did a clever third-order really improve that much over a different least squares algorithm? It shouldn't ... should it?
But what is interesting is that once the appropriate parameters are set, the best result can be obtained with the same parameters in the previous or subsequent blocks. Even if this depends on the shape of the data, in my tests it can sometimes be valid for hundreds of blocks before or after. you can see this by trying it immediately. Maybe we will also see the negative aspects of the method.