perception of instrumental blend is important for understanding aspects of orchestration, but no work has studied blends of impulsive and sustained instruments. The first experiment identified the factors that influence the rating of blendedness of dyads formed of one sustained sound and one impulsive sound. Longer attack times and lower spectral centroids increased blend. The contribution of the impulsive sound's properties to the degree of blend was greater than that of the sustained sound. The second experiment determined the factors that influence similarity ratings among dyads. The mean spectral envelope and the attack time of the dyad best explained the dissimilarity ratings. However, contrary to the first experiment, the spectral envelope of the sustained sound was more important than that of the impulsive sound. Multidimensional scaling of dissimilarity ratings on blended dyads yielded one dimension correlated with the attack time of the dyad and another dimension whose spectral correlate was different for two different clusters within the space, spectral spread for one and spectral flatness for the other, suggesting a combined categorical-analogical organization of the second dimension.
In this paper we first introduce a set of functions to predict the timbre features of an instrument sound combination, given the features of the individual components in the mixture. We then compare, for different classes of sound combinations, the estimated values of the timbre features to real measurements and show the accuracy of our predictors. In the second part of the paper, we present original musical applications of feature prediction in the field of computer-aided orchestration. These examples all come from real-life compositional situations, and were all produced with Orchide´e, an innovative framework for computer-aided orchestration recently designed and developed at IRCAM, Paris.
Abstract. In this paper we introduce an hybrid evolutionary algorithm for computer-aided orchestration. Our current approach to orchestration consists in replicating a target sound with a set of instruments sound samples. We show how the orchestration problem can be viewed as a multi-objective 0/1 knapsack problem, with additional constraints and a case-specific criteria formulation. Our search method hybridizes genetic search and local search, for both of which we define ad-hoc genetic and neighborhood operators. A simple modelling of sound combinations is used to create two new mutation operators for genetic search, while a preliminary clustering procedure allows for the computation of sound mixtures neighborhoods for the local search phase. We also show in which way user interaction might be introduced in the orchestration procedure itself, and how to lead the search according to the users choices.
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