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Research Papers

Dynamic Acoustic Measurement Techniques Considering Human Perception

[+] Author and Article Information
Klaus Genuit

 HEAD acoustics GmbH, Ebertstrasse 30a, D-52134 Herzogenrath-Kohlscheid, Germany

Wade Bray1

 HEAD acoustics, Inc., 6964 Kensington Road, Brighton, MI 48116wbray@headacoustics.com

1

Corresponding author.

J. Vib. Acoust 130(3), 031005 (Apr 03, 2008) (12 pages) doi:10.1115/1.2827453 History: Received February 15, 2007; Revised September 17, 2007; Published April 03, 2008

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. The human ear/brain system exceeds the simultaneous time and frequency recognition of conventional measurement methods, further complicating the challenge. People have at least three times better 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 an 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 (Sottek, R., 1993, “Modelle zur Signalverarbeitung im menschlichen Gehör  ,” dissertation, RWTH Aachen), a relative (pattern) measurement technique subtracting a sliding average in both time and frequency from a running instantaneous spectrum (Genuit, K., 1996, “A New Approach to Objective Determination of Noise Quality Based on Relative Parameters  ,” Proceedings of InterNoise, Liverpool, UK), 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 (Sottek, R., 1993, “Modelle zur Signalverarbeitung im menschlichen Gehör  ,” dissertation, RWTH Aachen;Bray, W. R., 2004, “Perceptually Related Analysis of Time-Frequency Patterns via a Hearing Model (Sottek), a Pattern-Measurement Algorithm (“Relative Approach”) and a Window-Deconvolution Algorithm  ,” 147th Meeting, New York, May, Acoustical Society of America, 5aPPb7). Types of noise which particularly benefit from the techniques we will discuss include, but are by no means limited to, time-varying emissions from information technology devices (printers, hard disk drives, servosystems), appliances, HVAC (compressors and controls), hydraulic systems including direct high-pressure fuel injection internal combustion engines, tonal orders from rotating machinery, and environmental noise in workplaces and residences. The three analytic tools presented here are well suited in matching the time-frequency, tonal, and pattern recognition capabilities of human hearing, and offer general engineering capabilities especially involving the fine time-structured behavior of transient and tonal events.

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Copyright © 2008 by American Society of Mechanical Engineers
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References

Figures

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Figure 8

Large wind turbine (600kVA): modulation spectrum versus critical bands (upper), spectrum versus time (lower)

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Figure 9

Windowed (Hanning) FFT versus time (left), no window (middle), window deconvolved, and frequency resolution multiplied 16× (right)

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Figure 10

Impact response (acoustic) of annular disk; FFT versus time, best setting (upper), high-resolution spectrum versus time (lower)

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Figure 11

Amplitude- and frequency-modulated tones in tire tread noise by FFT versus time (upper) and high-resolution spectral analysis (lower)

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Figure 1

A FFT versus time measurement of rattles; block size (2048 points) and sampling rate (44.1kHz) resolve time structure, but do not fully represent peak levels

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Figure 2

Hearing model functional block diagram

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Figure 3

Door closure; hearing model (top), wavelet (middle), FFT versus time (256 points) (lower)

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Figure 4

Hard disk drive spin down to a stop, by FFT versus time (upper) and hearing model (lower); identical scales

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Figure 5

Relative approach spectrum versus time of a diesel engine (upper), and the same with random noise added (lower)

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Figure 6

Tailpipe measurement of a high-performance automotive engine runup at full throttle: FFT versus rpm (upper), hearing model (middle), relative approach (lower)

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Figure 7

Modulation spectrum versus band, diesel engine; signal divided into critical bands (upper), 1∕3-octave bands (lower)

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Figure 12

Startup of air-conditioning compressor

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