The determination of structural boundaries is a key task for understanding the structure of a musical piece, but it is also highly ambiguous. Recently, Convolutional Neural Networks (CNN) trained on spectrogram features and human annotations have been successfully used to tackle the problem, but still fall clearly behind human performance. We expand on the CNN approach by combining spectrograms with self-similarity lag matrices as audio features, thereby capturing more facets of the underlying structural information. Furthermore, in order to consider the hierarchical nature of structural organization, we explore different strategies to learn from the two-level annotations of main and secondary boundaries available in the SALAMI structural annotation dataset. We show that both measures improve boundary recognition performance, resulting in a significant improvement over the previous state of the art. As a side-effect, our algorithm can predict boundaries on two different structural levels, equivalent to the training data.
['Thomas Grill', 'Jan Schl']
MUSIC BOUNDARY DETECTION USING NEURAL NETWORKS ON COMBINED FEATURES AND TWO-LEVEL ANNOTATIONS