Semi-supervised learning for musical instrument recognition

In this work, the semi-supervised learning (SSL) techniques are explored in the context of musical instrument recognition. The conventional supervised approaches normally rely on annotated data to train the classifier. This implies performing costly manual annotations of the training data. The SSL methods enable utilising the additional unannotated data, which is significantly easier to obtain, allowing the overall development cost maintained at the same level while notably improving the performance. The implemented classifier incorporates the Gaussian mixture model-based SSL scheme utilising the iterative EM-based algorithm, as well as the extensions facilitating a simpler convergence criteria. The evaluation is performed on a set of nine instruments while training on a dataset, in which the relative size of the labelled data is as little as 15%. It yields a noteworthy absolute performance gain of 16% compared to the performance of the initial supervised models.