Paper Archive

Drum samples are an important ingredient for many styles of music. Large libraries of drum sounds are readily available. However, their value is limited by the ways in which users can explore them to retrieve sounds. Available organization schemes rely on cumbersome manual classification. In this paper, we present a new approach for automatically structuring and visualizing large sample libraries through audio signal analysis. In particular, we present a hierarchical user interface for efficient exploration and retrieval based on a computational model of similarity and self-organizing maps.

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Periodic or quasi-periodic low-frequency components (i.e. vibrato and tremolo) are present in steadystate portions of sustained instrumental sounds. If we are interested both in studying its expressive meaning, or in building a hierarchical multi-level representation of sound in order to manipulate it and transform it with musical purposes those components should be isolated and separated from the amplitude and frequency envelopes. Within the SMS analysis framework it is now feasible to extract high level time-evolving attributes starting from basic analysis data. In the case of frequency envelopes we can apply STFTs to them, then check if there is a prominent peak in the vibrato/tremolo range and, if it is true, we can smooth it away in the frequency domain; finally, we can apply an IFFT to each frame in order to re-construct an envelope that has been cleaned of those quasi-periodic low-frequency components. Two important problems nevertheless have to be tackled, and ways of overcoming them will be discussed in this paper: first, the periodicity of vibrato and tremolo, that is quite exact only when the performers are professional musicians; second: the interactions between formants and fundamental frequency trajectories, that blur the real tremolo component and difficult its analysis.

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