Aaron McLeran’s Granular Synthesis Piano Tranformation

Composer Aaron McLeran has posted a series of “digital etudes”, each based on a solo piano recording that has been treated to various granular synthesis techniques:

Until recently, a downside to granular synthesis was the fact that there was no good way to build meaningful granular models of sound. This is not the case with other synthesis techniques. For example, one can get an additive synthesis model for a given sound sample by deriving it’s Fourier Transform. The additive synthesis model in this case meaningfully represents the original sound.

New algorithms have emerged in the last few years which build just such a model for granular synthesis. One popular algorithm, called Matching Pursuit, iteratively matches a given signal with grains from a pre-determined dictionary (or set of parameter definitions) of grains. When the algorithm is finished, we are left with a granular model of the original signal which give us totally unique methods of sonic transformations. However, these algorithms are quite slow (much slower than real-time) and there are a number of poorly understood artifacts that need more study. It should be noted that while the analysis is slow, resynthesis is possible in real-time.

In addition to researching novel sound-design and musical applications for this technique, as well as developing software tools to aid in this endeavor, our research is also focused on improving the algorithms. Because of destructive interference between grains in our model, it usually contains more energy than is present in the signal we actually hear. Because we can’t hear it, but the energy is there, we call it Dark Energy. Most research in this field is focused on eliminating Dark Energy. We are interested in finding ways we might be able to use it to improve the algorithm or to inform our granular model transformations.

Here’s an example, with the original piano solo recording and the manipulated piece:


via disquiet

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