  Music segmentation refers to the dual problem of identifying boundaries
between, and labeling, distinct music segments, e.g., the chorus, verse, bridge
etc. in popular music. The performance of a range of music segmentation
algorithms has been shown to be dependent on the audio features chosen to
represent the audio. Some approaches have proposed learning feature
transformations from music segment annotation data, although, such data is time
consuming or expensive to create and as such these approaches are likely
limited by the size of their datasets. While annotated music segmentation data
is a scarce resource, the amount of available music audio is much greater. In
the neighboring field of semantic audio unsupervised deep learning has shown
promise in improving the performance of solutions to the query-by-example and
sound classification tasks. In this work, unsupervised training of deep feature
embeddings using convolutional neural networks (CNNs) is explored for music
segmentation. The proposed techniques exploit only the time proximity of audio
features that is implicit in any audio timeline. Employing these embeddings in
a classic music segmentation algorithm is shown not only to significantly
improve the performance of this algorithm, but obtain state of the art
performance in unsupervised music segmentation.
