In this paper we present a simple yet novel technique for harmonic/percussive separation of monaural audio music signals. Under the assumption that percussive/harmonic components exhibit vertical/horizontal lines in the spectrogram, image morphological filters are applied to the spectrogram of the input signal. The structure elements of the morphological filters are chosen to accentuate regions of the spectrogram corresponding to harmonic and percussive components. The proposed method was evaluated on the SISEC 2008/2010 development data and outperformed the baseline method adopted.

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This paper 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.

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