Learning curves show the learning rate of a clas- sifier by plotting the dataset size used to train the classifier versus the error rate. By extrapolating these curves it is possible to predict how well the classifier will perform when trained on dataset sizes that are currently not available. This can be useful when trying to determine which classifier to select when dealing with a classification problem. Ob- taining these learning curves is usually done by fit- ting a parametric model to the learning data. This paper analyzes the potential of fitting the curve in a different space scaling the fitting data. This is done by analyzing the accuracy of the fit and the frequency of the fit succeeding. Our main findings are that log scaling produces better MSEs than not scaling, while exponential scaling is inconclusive.

AI-generated music is a huge research field with many different approaches and models being the result of it. One such model is the ProceduraLiszt model, which utilizes the Wave Function Collapse algorithm, an algorithm similar to constraint programming, to generate its music. This research builds upon that model. It does so by trying to reverse engineer a given piece of music into a set of satisfied constraints that the model is compatible with. We present an approach that allows for the inference of constraints of a given music file that adheres to the MIDI format called MidiAnalyser. We run our model on a set of music files and analyze the inferred constraints. The constraints include aspects like key and note range.