Extending the Capacity of 1 / f Noise Generation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11008)


From the emissions of massive quasars scattered across the universe, to the fluctuations in the stock market and the melodies of music, several real world signals have a power spectral density (PSD) that follows an inverse relationship with their frequency. Specifically, this type of random process is referred to as a \(1/f\) signal, and has been of much interest in research, as sequences that have this property better mimic natural signals. In the context of constraint programming, a recent work has defined a constraint that enforces sequences to exhibit a \(1/f\) PSD, as well as a corresponding constraint propagator. In this paper we show that the set of valid solutions associated with this propagator misses an exponential number of \(1/f\) solutions and accepts solutions that do not have a \(1/f\) PSD. Additionally, we address these two issues by proposing two non-exclusive algorithms for this constraint. The first one can find a larger set of valid solutions, while the second prevents most non-\(1/f\) solutions. We demonstrate in our experimental section that using the hybrid of these two methods results in a more robust propagator for this constraint.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceCornell UniversityIthacaUSA

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