A regularized kernel-based approach to unsupervised audio segmentation

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Kernel Change-point Analysis (2008, methodology): “Note also that, instead of the Tikhonov-type regularization of the covariance operator, other regularization schemes may also be applied, such as the spectral truncation regularization of the covariance operator, equivalent to preprocessing by a centered kernel principal component analysis [20, 21],”
A Survey of Methods for Time Series Change Point Detection (2016, unknown): “The other method known as running maximum partition strategy [28] calculates the KFDR ratio between all consequent windows in each interval.”
Kernel-Based Methods for Hypothesis Testing: A Unified View (2013, methodology): “The data set used in [18] consists of both the audio and video recordings of the popular French 1980s talk shows (Le Grand Echiquier) of roughly three hours each.”
Selective review of offline change point detection methods (2020, background): “They are not directly estimated; instead, the statistics d(P̂x, P̂z) are 756 computed [11, 91, 99, 123].”
A review of change point detection methods (2018, methodology): “l family assumption [50,114], etc. In the more complex non-parametric setting, the distributionsareonlyassumedtobecontinuous. Theyarenotdirectlyestimated;instead,thestatistics d(bP x;Pbz) is computed [38,57,63,95]. In the context of single change point detection, the two-sample test setting is adapt”