Abstract: A computer-implemented method uses sales data to fit static parameters of a demand prediction model that predicts a current demand based in part on a previous demand. The static parameters and the sales data are then used to fit dynamic states of a structural time series model, wherein the dynamic states change over time and are different for different time periods. A time period for a future price is selected and the future price is applied to the structural time-series model using the dynamic states for the time period to generate an expected demand for the time period.

Abstract: The present disclosure is directed towards a method for speech intelligibility. The method may include receiving, at one or more computing devices, a first speech input from a first user and performing voice activity detection upon the first speech input. The method may also include analyzing a spectral tilt associated with the first speech input, wherein analyzing includes computing an impulse response of a linear predictive coding (“LPC”) synthesis filter in a linear pulse code modulation (“PCM”) domain and wherein the one or more computing devices includes an adaptive high pass filter configured to recalculate one or more linear prediction coefficients.

Abstract: The present disclosure is directed towards a method for speech intelligibility. The method may include receiving, at one or more computing devices, a first speech input from a first user and performing voice activity detection upon the first speech input. The method may also include analyzing a spectral tilt associated with the first speech input, wherein analyzing includes computing an impulse response of a linear predictive coding (“LPC”) synthesis filter in a linear pulse code modulation (“PCM”) domain and wherein the one or more computing devices includes an adaptive high pass filter configured to recalculate one or more linear prediction coefficients.