Greek content is currently not available

Towards an Invertible Rhythm Representation

icon pencil

This work investigates the development of a rhythm representation of music audio signals, that (i) is able to tackle rhythm related tasks and, (ii) is invertible, i.e. is suitable to reconstruct audio from it with the corresponding rhythm content being preserved. A conventional front-end processing schema is applied to the audio signal to extract time varying characteristics (accent features) of the signal. Next, a periodicity analysis method is proposed that is capable of reconstructing the accent features. Afterwards, a network consisting of Restricted Boltzmann Machines is applied to the periodicity function to learn a latent representation.

This latent representation is finally used to tackle two distinct rhythm tasks, namely dance style classification and meter estimation. The results are promising for both input signal reconstruction and rhythm classification performance. Moreover, the proposed method is extended to generate random samples from the corresponding classes.


Click here to get access to some examples of the reconstructed music excerpts.

Related work

Aggelos Gkiokas, Vassilis Katsouros, Stefan Lattner, Arthur Flexer, and George Carayannis. "Towards an Invertible Rhythm Representation." In 18th International Conference on Digital Audio Effects (DAFx-15), Trondheim, Norway, 2015.