Browsing by Subject "1/f noise"
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(2024)The presence of 1/f type noise in a variety of natural processes and human cognition is a well-established fact, and methods of analysing it are many. Fractal analysis of time series data has long been subject to limitations due to the inaccuracy of results for small datasets and finite data. The development of artificial intelligence and machine learning algorithms over the recent years have opened the door to modeling and forecasting such phenomena as well which we do not yet have a complete understanding of. In this thesis principal component analysis is used to detect 1/f noise patterns in human-played drum beats typical to a style of playing. In the future, this type of analysis could be used to construct drum machines that mimic the fluctuations in timing associated with a certain characteristic in human-played music such as genre, era, or musician. In this study the link between 1/f-noisy patterns of fluctuations in timing and the technical skill level of the musician is researched. Samples of isolated drum tracks are collected and split into two groups representing either low or high level of technical skill. Time series vectors are then constructed by hand to depict the actual timing of the human-played beats. Difference vectors are then created for analysis by using the least-squares method to find the corresponding "perfect" beat and subtracting them from the collected data. These resulting data illustrate the deviation of the actual playing from the beat according to a metronome. A principal component analysis algorithm is then run on the power spectra of the difference vectors to detect points of correlation within different subsets of the data, with the focus being on the two groups mentioned earlier. Finally, we attempt to fit a 1/f noise model to the principal component scores of the power spectra. The results of the study support our hypothesis but their interpretation on this scale appears subjective. We find that the principal component of the power spectra of the more skilled musicians' samples can be approximated by the function $S=1/f^{\alpha}$ with $\alpha\in(0,2)$, which is indicative of fractal noise. Although the less skilled group's samples do not appear to contain 1/f-noisy fluctuations, its subsets do quite consistently. The opposite is true for the first-mentioned dataset. All in all, we find that a much larger dataset is required to construct a reliable model of human error in recorded music, but with the small amount of data in this study we show that we can indeed detect and isolate defining rhythmic characteristics to a certain style of playing drums.
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(2023)Low-frequency $1/$ noise is ubiquitous, found in all electronic devices and other diverse areas such as as music, economics and biological systems. Despite valiant efforts, the source of $1/f$ noise remains one of the oldest unsolved mysteries in modern physics after nearly 100 years since its initial discovery in 1925. In metallic conductors resistance $1/f$ noise is commonly attributed to diffusion of mobile defects that alter the scattering cross section experienced by the charge carriers. Models based on two-level tunneling systems (TLTS) are typically employed. However, a model based on the dynamics of mobile defects forming temporary clusters would naturally offer long-term correlations required by $1/f$ noise via the nearly limitless number of configurations among a group of defects. Resistance $1/f$ noise due to such motion of mobile defects was studied via Monte Carlo simulations of a simple resistor network resembling an atomic lattice. The defects migrate through the lattice via thermally activated hopping motion, causing fluctuations in the resistance due to varying scattering cross section. The power spectral density (PSD) $S(f)$ of the simulated resistance noise was then calculated and first compared to $S(f)=C/f^\alpha$ noise, where $C$ is a constant and $\alpha$ is ideally close to unity. The value of $\alpha$ was estimated via a linear fit of the noise PSD on a log-log scale. The resistor network was simulated with varying values of temperature, system size and the concentration of defects. The noise produced by the simulations did not yield pure $1/f^\alpha$ noise, instead the lowest frequencies displayed a white noise tail, changing to $1/f^\alpha$ noise between $10^{-4}$ to $10^{-2}$~Hz. In this way the spectrum of the simulated noise resembles a Lorentzian. The value of $\alpha$ was found to be the most sensitive to temperature $T$, which directly affects the motion of the defects. At high $T$ the value of $\alpha$ was closer to 1, whereas at low $T$ it was closer to $1,5$. Varying the size of the system was found to have little impact on $\alpha$ when the temperature and concentration of defects were kept fixed. Increasing the number of defects was found to have slightly more effect on $\alpha$ when the temperature and system size were kept fixed. The value of $\alpha$ was closer to unity when the concentration of defects was higher, but the effect was not nearly as pronounced compared to varying the temperature. In addition, the simulated noise was compared to a PSD of the form $S(f)\propto e^{-\sqrt{N}/T}1/f$, where $N$ is the size of the system, according to recent theoretical proceedings. The $1/f^\alpha$ part of the simulated noise was found to roughly follow the above equation, but the results remain inconclusive. Although the simple toy model did not produce pure $1/f^\alpha$ noise, the dynamics of the mobile defects do seem to have an effect on the noise PSD, yielding noise closer to $1/f$ when there are more interactions between the defects due to either higher mobility or higher concentration of defects. However, this is disregarding the white noise tail. Recent experimental research on high quality graphene employing more rigorous kinetic Monte Carlo simulations have displayed more promising results. This indicates that the dynamics of temporary cluster formation of mobile defects is relevant to understand $1/f$ noise in metallic conductors, offering an objective for future work.
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