  The ability of deep neural networks to learn complex data relations and
representations is established nowadays, but it generally relies on large sets
of training data. This work explores a "piece-specific" autoencoding scheme, in
which a low-dimensional autoencoder is trained to learn a latent/compressed
representation specific to a given song, which can then be used to infer the
song structure. Such a model does not rely on supervision nor annotations,
which are well-known to be tedious to collect and often ambiguous in Music
Structure Analysis. We report that the proposed unsupervised auto-encoding
scheme achieves the level of performance of supervised state-of-the-art methods
with 3 seconds tolerance when using a Log Mel spectrogram representation on the
RWC-Pop dataset.
