Marcelo F. Caetano

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Computer generated sounds for music applications have many facets, of which timbre design is of groundbreaking significance. Timbre is a remarkable and rather complex phenomenon that has puzzled researchers for a long time. Actually, the nature of musical signals is not fully understood yet. In this paper, we present a sound synthesis method using an(More)
There has been a great collective effort in the search for perceptually meaningful sound transformation techniques. The transformation of sounds matching target sound descriptors is a promising candidate because the descriptors are thought to capture timbral dimensions corresponding to relevant perceptual features. However, matching the descriptors alone is(More)
In this paper, we present a sound synthesis method that utilizes evolution as generative paradigm. Such sounds will be thereon referred to as evolutionary sounds. Upon defining a population of complex sounds, i.e. sound segments sampled from acoustical instruments and speech; we generated sounds that resulted from evolution applied to those populations. The(More)
The model used to represent musical instrument sounds plays a crucial role in the quality of sound transformations. Ideally, the representation should be compact and accurate, while its parameters should give flexibility to independently manipulate perceptually related features of the sounds. This work describes a source-filter model for musical instrument(More)
The aim of sound morphing is to obtain a result that falls perceptually between two (or more) sounds. In order to do this, we should be able to morph perceptually relevant features of sounds instead of blindly interpolating the parameters of a model. In this work we present automatic timbral morphing techniques applied to musical instrument sounds using(More)
We present a time domain approach to explore a sound transformation paradigm for musical performance. Given a set of sounds containing a priori desired qualities and a population of agents interacting locally, the method generates both musical form and matter resulting from sonic trajectories. This proposal involves the use of bio-inspired algorithms, which(More)
Music is widely perceived as expressive of emotion. However, there is no consensus on which factors in music contribute to the expression of emotions, making it difficult to find robust objective predictors for music emotion recognition (MER). Currently, MER systems use supervised learning to map non time-varying feature vectors into regions of an emotion(More)