Curtis Roads

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Although the boundaries of artificial intelligence (AI) remain elusive, computers can now perform musical tasks that were formerly associated exclusively with naturally intelligent musicians. After a historical note, this paper sermonizes on the need for AI techniques in four areas of musical research: composition, performance, music theory, and digital(More)
Sparse overcomplete methods, such as matching pursuit, attempt to find an efficient estimation of a signal using terms (atoms) selected from an overcomplete dictionary. In some cases, atoms can be selected that have energy in regions of the signal that have no energy. Other atoms are then used to destructively interfere with these terms in order to preserve(More)
As a combination of time and frequency modeling,sparse atomic representation provides a means for effective and interesting time-frequency modifications of signals. A drawback is the intensive computation required to find the representation. New tools however greatly speed the process and facilitate experimentation with real signals. We have experimented(More)
This article provides an overview of dictionary-based methods (DBMs), and reviews recent work in the application of such methods to working with audio and music signals. As Fourier analysis is to additive synthesis, DBMs can be seen as the analytical counterpart to a generalized granular synthesis, where a sound is built by combining heterogeneous atoms(More)
Sound particles or microsounds last only a few milliseconds, near the threshold of auditory perception. We can easily analyze the physical properties of sound particles either individually or in masses. However, correlating these properties with human perception remains complicated. One cannot speak of a single time frame, or a "time constant" for the(More)