within the existing literature include the oscillatory processes [@Priestley], Locally Stationary Fourier model [@LSF] and time-varying autoregressive processes [@dahlhaus1999nonlinear]. Due to the high frequency nature of acoustic sensing data, we focus our attention on wavelet-based methods such as the Locally Stationary Wavelet (LSW) processes, introduced by [@Nason00]. The use of wavelets allows for the time-scale decomposition of a signal using computationally efficient transform algorithms, whilst also allowing the structure to change over time.

Methods for the classification of non-stationary time series can broadly be divided into two categories; static or dynamic. Static classification approaches attempt to assign an entire test signal to a particular class. They differ in the way in which they choose to model the nonstationarity, including through Locally Stationary Fourier processes [@ST], the smooth localised exponentials (SLEX) framework [@Huang] and wavelets [@Fry; @Krz]. In contrast, dynamic classification approaches allow for the class assignment of the test signal to vary over time which allows for more flexibility in the classification and covers problems where the underlying nonstationarity is due to class switching. The method that we introduce is an online analogue of the dynamic classification approach of [@ParkDC] which looks to detect subtle changes in the dependence structure 