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The problem of target-based computer-aided orchestration is a recurring topic in the contemporary music community. Because of its complexity, computer-aided orchestration remains a partially unsolved problem and several systems have been developed in the last twenty years. This article presents a practical overview of the recently introduced Orchidea framework for dynamic computer-aided target-based orchestration. Orchidea continues the line of tools dedicated to the subject (the so-called Orchid* family) originally developed at the Institut de Recherche et Coordination Acoustique/Musique in Paris. Unlike its predecessors, Orchidea uses a combination of optimization techniques that include stochastic matching pursuit, long short-term memory neural networks, and monoobjective evolutionary optimization, with a specifically designed cost function. Symbolic constraints can be integrated in the cost function, and temporally evolving sounds are handled by segmenting them into a set of static targets optimized jointly and then connected. Orchidea is deployed in three different ways: a standalone application, designed to streamline a simplified compositional workflow; a Max package, targeted at composers willing to connect target-based orchestration to the more general area of computer-aided composition; and a set of command-line tools, mostly intended for research purposes and batch processing. The main aim of this article is to present an overview of such software systems and show several instances of the Orchidea framework's application in recent musical productions, tracing the path for future research on the subject.

This article introduces the Orchidea Orchestral Qualities framework (OOQ), an extension of the Orchidea environment for computer-aided orchestration. Traditional target-based orchestration generally reconstructs a target sound “as faithfully as possible” with a collection of samples. But more often than not, composers do not have specific targets in mind while performing orchestration tasks. A large class of orchestration practices deal with the transformation of musical material to enhance or reduce certain of its qualities (such as making a score more “brilliant,” “blurry,” “dense,” and so on). The OOQ framework implements this idea by making use of an analogy with digital signal processing. Scores and sounds are no longer used as targets, but rather as “sources” to be processed, not unlike what happens within a channel strip of a modern digital audio workstation. This article presents the rationale behind the OOQ framework, describes the behavior of its modules, and traces a path for future research on the subject.