Measuring a speaker’s ability to respond to an instantaneous pulse of energy will result in distortion at its output. Factors such as speaker geometry, material properties, equipment error, and the conditions of the environment will create artifacts within the captured data. This paper explores the extraction of time-domain features from these responses, and the training of a predictive model to allow for classification and rapid quality assurance.
