Aliasing is one of the most fundamental concepts in digital signal processing and directly impacts every acoustic measurement made with a digital system. When a continuous analog signal is sampled at a discrete rate, the Nyquist-Shannon sampling theorem states that only frequencies below half the sampling rate can be accurately represented. Any frequency component above this Nyquist limit "folds back" into the representable range, appearing as a phantom frequency that did not exist in the original signal.

In practical measurement terms, consider a system sampling at 48 kHz. The Nyquist frequency is 24 kHz. If a 26 kHz tone enters the system without filtering, it appears as a 22 kHz tone in the captured data — completely indistinguishable from a real 22 kHz signal. This aliased component corrupts spectrum analysis, distorts transfer function measurements, and introduces errors in RT60 calculations that rely on accurate frequency-domain data.

Professional measurement systems address aliasing through anti-aliasing filters — steep low-pass filters applied in the analog domain before the ADC. These filters typically begin rolling off a few kilohertz below the Nyquist frequency. In SonaVyx, the browser's Web Audio API handles anti-aliasing internally through the operating system's audio subsystem, but understanding the concept remains crucial when interpreting measurement results near the upper frequency limit.

Aliasing particularly affects measurements involving swept sine signals, MLS sequences, and any broadband excitation. When measuring speaker frequency response or room impulse response, aliased energy can appear as false peaks or elevated noise floors at specific frequencies. The transfer function and RT60 tools in SonaVyx process data well within the Nyquist limit to minimize aliasing artifacts.