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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Content description has become a topic of interest for many researchers in the audiovisual field [1][2]. While manual annotation has been used for many years in different applications, the focus now is on finding automatic contentextraction and content-navigation tools. An increasing number of projects, in some of which we are actively involved, focus on the extraction of meaningful features from an audio signal. Meanwhile, standards like MPEG7 [3] are trying to find a convenient way of describing audiovisual content. Nevertheless, content description is usually thought of as an additional information stream attached to the ‘actual content’ and the only envisioned scenario is that of a search and retrieval framework. However, in this article it will be argued that if there is a suitable content description, the actual content itself may no longer be needed and we can concentrate on transmitting only its description. Thus, the receiver should be able to interpret the information that, in the form of metadata, is available at its inputs, and synthesize new content relying only on this description. It is possibly in the music field where this last step has been further developed, and that fact allows us to think of such a transmission scheme being available on the near future.

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This paper presents an application for performing melodic transformations to monophonic audio phrases. The system first extracts a melodic description from the audio. This description is presented to the user and can be stored and loaded in a MPEG-7 based format. A set of high-level transformations can then be applied to the melodic description. These high-level transformations are mapped into a set of low-level signal transformations and then applied to the audio signal. The algorithms for description extraction and audio transformation are also presented.

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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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Similarity is a key concept for estimating associations among a set of objects. Music similarity is usually exploited to retrieve relevant items from a dataset containing audio tracks. In this work, we approach the problem of semantic similarity between short pieces of music by analysing their instrumentations. Our aim is to label audio excerpts with the most salient instruments (e.g. piano, human voice, drums) and use this information to estimate a semantic relation (i.e. similarity) between them. We present 3 different methods for integrating along an audio excerpt frame-based classifier decisions to derive its instrumental content. Similarity between audio files is then determined solely by their attached labels. We evaluate our algorithm in terms of label assignment and similarity assessment, observing significant differences when comparing it to commonly used audio similarity metrics. In doing so we test on music from various genres of Western music to simulate real world scenarios.

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