Dynamic measurement implies determining the content of signals having spectral structure and energy changing with time, sometimes on very short time scales. Dynamic measurements can present challenges to determine sufficient information in both the time and frequency domains. High resolution in frequency prevents finding short-term peak levels and recognizing true crest factors, and vice versa. If the dynamic measurement concerns sound, the much better simultaneous recognition of time and frequency information by the ear/brain than by conventional measurement methods can further complicate the challenge. People have at least three times better simultaneous time/frequency resolution than the familiar Fourier transform moved across the time axis, although quite often a compromise block size can be found that gives time/frequency measurement agreeing with human sound perception of both factors. Unlike technical measuring systems, human hearing is also very sensitive to patterns. The presence of tones, varying tones (amplitude and/or frequency), clicks, rattles, splashing sounds, etc., even at low levels in the presence of other less structured noise of considerably higher level, can dominate perception. Human consciousness effectively performs the opposite of averaging, ignoring the absolute value of slowly varying or stationary signals and focusing on things differing at short time bases from their surroundings in both time and frequency. In dynamic measurement it can be difficult to withdraw the important pattern from the absolute whole. Case studies will be given comparing conventional techniques with three high-resolution time/frequency methods useful in general engineering although developed to model the processes of human sound perception: a hearing model with very rapid time resolution at all frequencies [1], a relative (pattern) measurement technique subtracting a sliding average in both time and frequency from a running instantaneous spectrum [2], and a Fourier-based window deconvolution method giving pure spectral lines regardless of signal-to-block synchronization and permitting multiplication of frequency resolution for a given block length and time resolution [1], [3].

1.
Sottek, Roland; Modelle zur Signalverarbeitung im menschlichen Geho¨r (dissertation, RWTH Aachen, 1993)
2.
Genuit, Klaus; A New Approach to Objective Determination of Noise Quality Based on Relative Parameters (Proc. InterNoise 1996, Liverpool, U. K.)
3.
Bray, W. R.; Perceptually related analysis of time-frequency patterns via a hearing model (Sottek), a pattern-measurement algorithm (“Relative Approach”) and a window-deconvolution algorithm (5aPPb7, Acoustical Society of America, 147th Meeting, May 2004, New York City
4.
Zwicker, E., and H. Fastl; Psychoacoustics: Facts and Models (Springer-Verlag, Germany, 1999)
5.
Acoustics — Method for calculating loudness level, International Standard ISO 532B:1975 (International Organization for Standardization, Geneva, Switzerland)
6.
Fastl, H., and W. Schmid; Comparison of Loudness Analysis Systems, (Proc. InterNoise 1997, Budapest, Hungary) p. 981–986
7.
Ali, Dave, and W. Bray; Characterization of Run-up and Down Transient Events for a Hard Disk Drive (Proceedings, NoiseCon 2004, pp.785–794)
8.
Bray, W. R.; The “Relative Approach” for Direct Measurement of Noise Patterns (Sound & Vibration, September 2004, pp. 20–23)
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