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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 toreduce this burden with automation. State-of-the-art systems for estimatingcompatibility, such as AutoMashUpper, are mostly rule-based and could beimproved on with machine learn-ing. To train a model, we need a large set ofloops with ground truth compatibility values. No such dataset exists, so weextract loops from existing music to obtain positive examples of compatibleloops, and propose and compare various strategies for choosing negativeexamples. For re-producibility, we curate data from the Free MusicArchive.Using this data, we investigate two types of model architectures forestimating the compatibility of loops: one based on a Siamese network, and theother a pure convolutional neural network (CNN). We conducted a user study inwhich participants rated the quality of the combinations suggested by eachmodel, and found the CNN to outperform the Siamese network. Both model-basedapproaches outperformed the rule-based one. We have opened source the code forbuilding the models and the dataset.

Neural Loop Combiner: Neural Network Models for Assessing the Compatibility of Loops