An RMS detector uses the concept of the k-NN (classifying using nearest neighbors)—algorithm in order to obtain RMS values. A rms detector using first-order regressor with a variable smoothing factor is modified to penalize samples from center of data in order to obtain RMS values. Samples which vary greatly from the background noise levels, such as speech, scratch, wind and other noise spikes, are dampened in the RMS calculation. When background noise changes, the system will track the changes in background noise and include the changes in the calculation of the corrected RMS value. A minimum tracker runs more often (e.g. two or three times) than the rate as in prior art detectors and methods, tracks the minimum rms value, which is to compute a normalized distance value, which in turn is used to normalize the smoothing factor. From this data, a corrected or revised RMS value is determined as the function of the previous RMS value multiplied by one minus the smoothing factor plus the smooth factor times the minimum rms value to output the corrected RMS for the present invention. The rms value is used to generate a reset signal for the minimum tracker and is used to avoid deadlock in the tracker, for example, when the background signal increases/decreases over time.