Several different test signals with flat power spectrum, such as white noise, Schroeder-phased waveform, and Galois noise sequences were generated and used to adaptively model the error path in an active sound cancellation (ASC) controller. The standard filtered-X LMS (FXLMS) algorithm was used with a Finite Impulse Response (FIR) model of the error path and an FIR controller structure. Both adaptive FIR filters were operated during the active sound cancellation of 80 and 125 Hz tones. The residual modeling error for different test signal mean power spectral densities (PSD) was determined. It was discovered that the residual error decreased with an increase in the mean PSD, until a minimum residual error was achieved. When the mean PSD was increased beyond this value, no further decrease in residual error occurred. Each test signal was distinguished by the minimum mean PSD that achieved the minimum residual error. All three test signals achieved approximately the same residual error at approximately the same mean PSD for both the 80 and 125 Hz tone. Therefore, the best test signal was selected as the signal with the lowest peak factor, in this case the Schroeder-phased waveform. Given a low peak factor, power can be injected into the test waveform without significantly increasing the size of the time domain envelope.