In our current project, vocal signal has to be used to drive sound synthesis. In order to study the mapping between voice and synthesis parameters, the inverse problem is first studied. A set of reference synthesizer sounds have been created and each sound has been imitated by a large number of people. Each reference synthesizer sound belongs to one of the six following morphological categories: “up”, “down”, “up/down”, “impulse”, “repetition”, “stable”. The goal of this paper is to study the automatic estimation of these morphological categories from the vocal imitations. We propose three approaches for this. A base-line system is first introduced. It uses standard audio descriptors as inputs for a continuous Hidden Markov Model (HMM) and provides an accuracy of 55.1%. To improve this, we propose a set of slope descriptors which, converted into symbols, are used as input for a discrete HMM. This system reaches 70.8% accuracy. The recognition performance has been further increased by developing specific compact audio descriptors that directly highlight the morphological aspects of sounds instead of relying on HMM. This system allows reaching the highest accuracy: 83.6%.

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Swing is a typical long-short rhythmical pattern that is mostly present in jazz music. In this article, we propose an algorithm to automatically estimate how much a track, a frame of a track, is swinging. We denote this by swing ratio. The algorithm we propose is based on the analysis of the auto-correlation of the onset energy function of the audio signal and a simple set of rules. For the purpose of the evaluation of this algorithm, we propose and share the “GTZAN-rhythm” test-set, which is an extension of a well-known test-set by adding annotations of the whole rhythmical structure (downbeat, beat and eight-note positions). We test our algorithm for two tasks: detecting tracks with or without swing, and estimating the amount of swing. Our algorithm achieves 91% mean recall. Finally we use our annotations to study the relationship between the swing ratio and the tempo (study the common belief that swing ratio decreases linearly with the tempo) and the musicians. How much and how to swing is never written on scores, and is therefore something to be learned by the jazzstudents mostly by listening. Our algorithm could be useful for jazz student who wants to learn what is swing.

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