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Recent applied mathematics research on the properties of the invertible shift-invariant discrete wavelet transform has produced new ways to visualize, separate, and synthesize impulsive sounds, such as thuds, slaps, taps, knocks, and rattles. These new methods can be used to examine the joint time-frequency characteristics of a sound, to select individual components based on their time-frequency localization, to quantify the components, and to synthesize new sounds from the selected components. The new tools will be presented in a non-mathematical way illustrated by two real-life sound quality problems, extracting the impulsive components of a windshield wiper sound, and analyzing a door closing-induced rattle.

One of the primary correlates to customer annoyance with door-closing sound is peak loudness. In addition, customer annoyance also increases with the existence of secondary impacts, such as rattles. While these secondary impacts are typically not seen in the time-varying loudness trace (or other common sound quality metrics), it is often possible to visually identify the impacts in a time-frequency display of the door-closing sound. But the reduction of this display information to a single-number objective metric that agrees with subjective assessments has previously proved elusive. This paper summarizes the recent development and application of an objective metric that agrees with subjective classifications of secondary impacts in door-closing sounds.