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Author: Patricia Davies
Product-design engineers should be educated in measuring and analyzing how people perceive noise.
Engineered systems produce both wanted and unwanted products. For example, a computer printer produces text and images but also produces noise in the process. Yet, in the printer-design process, people’s perceptions of the quality of the noise and the quality of the images are often left out of the definition of what constitutes a good printer. In the past, designers have striven to design printers that produced exact replications of original images. In fact, it would be more effective to focus on replicating the characteristics of the image that are important to the observer (Wu et al., 2001).
Once the imaging problem has been solved, designers must address the noise problem. Merely reducing the level of noise may not be sufficient, because even soft sounds may be annoying if they fluctuate (changing sounds tend to attract attention). However, people might like to hear that the printer is operating, so eliminating noise altogether is not desirable either. The sound must meet people’s expectations.
In general, noise control should be focused on reducing the sound attributes that are most annoying or that cause acoustical discomfort and enhancing the sound attributes that people wish to hear. This can be difficult, however, if several outputs or different audiences compete rather than complement each other. In the case of the printer, for example, increased speed may mean increased noise and lower print quality. Or desirable sound for the person awaiting the printed page may be annoying or worse to the person working close to the printer. How should designers make these trade-offs?
Clearly, “people-sensitive” criteria must be included in the design process. However, the development of these criteria is problematic for most designers, because they are rarely educated in measuring and analyzing how people perceive the outputs of their products. Thus, if end users’ opinions are considered, they are usually tested with ad hoc methodologies developed without the help of psychologists, who are trained in making these types of measurements. In addition, testing is usually done so late in the design process that significant changes are no longer feasible. Whereas an integrated design approach might not lead to higher cost, add-on solutions at the end of the process always increase cost.
Perception-based engineering is a term coined by professors at Purdue who were working on a proposal that involved a number of existing, collaborative research projects between engineering and psychology, such as automatic speech recognition, control of machine noise, automotive interior design, air quality, touch interfaces, printer design, and highway design. Although at first glance these projects may not have much in common, all of them were focused on getting a better understanding of how people perceive, process, and make decisions in response to intended or unintended stimuli—usually generated by machines or engineered systems. For example, the text and images produced by a printer are intended outputs whereas noise is an unintended output.
In perception-based research, engineers and psychologists work together to develop models of human information processing and decision making. By connecting engineering stimulus-prediction models to perception models and decision-making models, design options can be explored, compared, and optimized to reduce negative impacts and accentuate positive impacts.
Figure 1 is a sample of this model-building process from a study of how combustion variability impacts the sound quality of diesel engines (Hastings, 2004). Chen and Chiu (2004) used a similar model to address the perception of color banding in printed images (stripes that appear in the image in the print direction). Their goal was to design a printer-control algorithm to compensate for imperfections in printer components, thus removing perceptible banding in the printed images.
Engineers do not always
use appropriate metrics to
quantify human responses.
An understanding of effective human information processing can be extremely helpful in the development of “perceptive” machines—for example, word-recognition machines focused on the automatic detection of nouns and names (Surprenant et al., 1999); studies of multimodal gestures and speech (Harper and Shriberg, 2004); and haptics research being used to develop tactile displays of speech for people with hearing and visual impairments (Israr et al., 2006).
The overall purpose of perception-based engineering research is to integrate the ways people perceive, and are affected by, machinery outputs into the design of engineered systems: