https://phys.org/news/2018-11-deconstructing-crowd-noise-college-basketball.html > "Crowd noise is typically treated as background interference—something to screen out." But the BYU researchers felt that crowd noise was worthy of its own investigation. In particular, they wanted to see whether machine learning algorithms could pick out patterns within the raw acoustical data that indicated what the crowd was doing at a given time, thereby providing clues as to what was happening in the game itself. One possible application of this could be the early detection of unruly or violent crowd behavior—though that idea has not been tested. > The BYU team made high-fidelity acoustic measurements during men's and women's basketball games at the university, later doing the same for football and volleyball games. They broke up the games into half-second intervals, measuring the frequency content (as displayed on spectrograms), sound levels, the ratio of the maximum to minimum sound levels within a set time block, and other variables. Then they applied signal processing tools that identified 512 distinct acoustical features comprised of different frequency bands, amplitudes and so forth.