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Concatenative sound synthesis is a promising method of musical sound synthesis with a steady stream of work and publications for over five years now. This article offers a comparative survey and taxonomy of the many different approaches to concatenative synthesis throughout the history of electronic music, starting in the 1950s, even if they weren't known as such at their time, up to the recent surge of contemporary methods. Concatenative sound synthesis methods use a large database of source sounds, segmented into units, and a unit selection algorithm that finds the units that match best the sound or musical phrase to be synthesised, called the target. The selection is performed according to the descriptors of the units. These are characteristics extracted from the source sounds, e.g. pitch, or attributed to them, e.g. instrument class. The selected units are then transformed to fully match the target specification, and concatenated. However, if the database is sufficiently large, the probability is high that a matching unit will be found, so the need to apply transformations is reduced. The most urgent and interesting problems for further work on concatenative synthesis are listed concerning segmentation, descriptors, efficiency, legality, data mining, and real time interaction. Finally, the conclusion tries to provide some insight into the current and future state of concatenative synthesis research.

This paper describes our attempt to make the Hidden Markov Model (HMM) score following system developed at Ircam sensible to past experiences in order to obtain better audio to score real-time alignment for musical applications. A new observation modeling based on Gaussian Mixture Models is developed which is trainable using a learning algorithm we would call automatic discriminative training. The novelty of this system lies in the fact that this method, unlike classical methods for HMM training, is not concerned with modeling the music signal but with correctly choosing the sequence of music events that was performed. Besides obtaining better alignment, new system's parameters are controllable in a physical manner and the training algorithm learns different styles of music performance as discussed.

A widespread belief is that large groups engaged in joint actions that require a high level of flexibility are unable to coordinate without the introduction of additional resources such as shared plans or hierarchical organizations. Here, we put this belief to a test, by empirically investigating coordination within a large group of 16 musicians performing collective free improvisation—a genre in which improvisers aim at creating music that is as complex and unprecedented as possible without relying on shared plans or on an external conductor. We show that musicians freely improvising within a large ensemble can achieve significant levels of coordination, both at the level of their musical actions (i.e., their individual decisions to play or to stop playing) and at the level of their directional intentions (i.e., their intentions to change or to support the music produced by the group). Taken together, these results invite us to reconsider the range and scope of actions achievable by large groups, and to explore alternative organizational models that emphasize decentralized and unscripted forms of collective behavior.

Joint actions typically involve a sense of togetherness that has a distinctive phenomenological component. While it has been hypothesized that group size, hierarchical structure, division of labour, and expertise impact agents' phenomenology during joint actions, the studies conducted so far have mostly involved dyads performing simple actions. We explore in this study the complex case of collectively improvised musical performances, focusing particularly on the way group size and interactional patterns modulate the phenomenology of joint action. We recorded two expert improvisation ensembles of contrasting sizes (16 vs 4 musicians) and collected data about their musical behaviour, as well as reports about five aspects of their phenomenology (sense of agency, agentive identity, integration, dependence, reflexivity) and about their musical intentions. Our overall data enabled us to assess how those five phenomenological dimensions related to one another during jointly improvised performances. They also show how such phenomenology varied with the way improvisers dynamically related to one another throughout the performance. Finally, we observe that group size strongly altered the phenomenology of improvisers who otherwise shared many characteristics (high expertise, similar aesthetic preferences, etc.). Our study thus sheds light on the interactional and structural parameters that shape and modulate our felt experience when acting together, and thereby highlights the importance of pluralism for studying the phenomenology of joint action.