  Music producers who use loops may have access to thousands in loop libraries,
but finding ones that are compatible is a time-consuming process; we hope to
reduce this burden with automation. State-of-the-art systems for estimating
compatibility, such as AutoMashUpper, are mostly rule-based and could be
improved on with machine learn-ing. To train a model, we need a large set of
loops with ground truth compatibility values. No such dataset exists, so we
extract loops from existing music to obtain positive examples of compatible
loops, and propose and compare various strategies for choosing negative
examples. For re-producibility, we curate data from the Free Music
Archive.Using this data, we investigate two types of model architectures for
estimating the compatibility of loops: one based on a Siamese network, and the
other a pure convolutional neural network (CNN). We conducted a user study in
which participants rated the quality of the combinations suggested by each
model, and found the CNN to outperform the Siamese network. Both model-based
approaches outperformed the rule-based one. We have opened source the code for
building the models and the dataset.
