A Scientific Approach Towards Training Listeners
To address this need, the author has developed a critical listening course called How to Listen. The course aims to teach students how to evaluate sound quality using percepts well established in the auditory perception field. These sound quality percepts are taught and demonstrated in a controlled way using real-time processing of recorded sounds. This has two benefits. First, the intensity of each attribute can be adjusted according to the aptitude and performance of the listener. Second, closely tying the physical properties of the stimulus to its perception and evaluation (a science known as psychoacoustics) there is theoretical basis behind the training approach. For example, the listener training data can be used to better understand how we perceive sound quality, which physical aspects of sound matter most of its perceived quality, and possibly identify the important underlying sound quality attributes that influence our preferences. Critical listening is treated as a science, rather than the black art it currently is.
How to Listen also includes classroom topics in the fundamentals of human auditory perception, sound quality research in variables that significantly influence the quality of recorded and reproduced sound (e.g. loudspeakers, rooms, recordings, microphones) and a brief tutorial in how to conduct sound quality listening tests that produce accurate, reliable and valid results.
But before we get too far ahead of ourselves, there must be good reasons for training listeners since it requires an investment in time and resources. There is also the question of external validity: Can the sound quality preferences of trained listeners be extrapolated to the preferences of untrained listeners, and does this hold true across different cultures? These questions will be answered in the following sections.
Why Train Listeners?
There are several compelling reasons for training listeners. First, trained listeners have been shown to produce more discriminating and reliable judgment of sound quality than untrained listeners . Fewer listener can be used to achieve a similar level of statistical confidence, which can result in savings in time and money. For example, a panel of 15 trained listeners can provide sound quality ratings with reliable statistical confidence in less than 8 hours. To achieve a similar level of confidence using untrained listeners would require about 10 times more listeners, 10 times more days to complete the testing, and cost 10 times more money to pay the listeners and staff conducting the tests. If the study is conducted by an independent research firm using 200-300 untrained listeners, the cost can easily exceed $100k.
A second reason for training listeners is that they are able to report precisely what they like and dislike about the sound quality using well-defined, meaningful terms. This feedback can provide important guidance for reengineering the product for optimal sound quality.
Besides training listeners for product research, there are benefits in training audio marketing and sales people to become better critical listeners. Training makes them better equipped to communicate sound quality issues to audio engineers and customers. As audio companies expand sales and operations in China, India, and other developing countries, there is a growing need to develop a common cross-cultural understanding as to what constitutes good sound and unacceptable sound.
Does Training Bias Listeners?
An important question is whether the training process itself biases the sound quality preferences of listeners. If the trained listener preferences are different from those of the targeted demographic, there is a danger the product may not be well received in the marketplace. This raises the age old question, “Is preference in sound quality a matter of personal taste - much like food, wine and music - or is it universal?”
To study this question, the author compared the performances and loudspeaker preferences of trained listeners versus untrained listeners . Over 300 untrained listeners were tested over a period of 18 months where they compared four different loudspeakers under controlled, double-blind listening conditions. Their preferences were then compared to the preferences of the trained Harman listening panel.
The results, plotted in Figure 2, show that the rank ordering of the loudspeaker preferences were the same for both the trained and untrained listeners. There were two main differences in how the two groups of listeners responded. First, the trained listeners tended to give lower loudspeaker ratings overall. Second, the trained listeners distinguished themselves from the untrained listeners by generally giving more discriminating and consistent loudspeaker preference ratings.
|Figure 2: The mean loudspeaker preference ratings and 95% confidence intervals are shown for four loudspeakers evaluated in a controlled, double-blind listening test. The results of different groups of untrained listeners are compared to those of the 12 Harman listeners.|
Relative Performances of Trained Versus Untrained Listeners
A common performance metric used to quantify the listener’s discrimination and consistency in rating sound quality is the F-statistic. This calculation is done by performing an analysis of variance (ANOVA) on the main variable being tested. In the above study , the performances of trained versus untrained listeners were compared by calculating the loudspeaker F-statistic for each individual listener. Figure 3 shows the relative performance of different groups of untrained listeners based on their mean F-statistics compared to the F-statistics of the trained listeners. The relative performances of the untrained groups were: audio retailers (35%), audio reviewers (20%), audio marketing/sales staff (10%), and college students (4%). The poor performance of the students was explained by their tendency to give all four loudspeakers very similar and high ratings. A likely explanation for this was that they experienced a level of sound quality that was much higher than their everyday common experience: compressed MP3 music reproduced through headphones. The good news is that the students seemed to appreciate the higher fidelity sound based on the high ratings. In time, they will hopefully seek out better quality audio systems.
|Figure 3: The relative performance of different groups of untrained listeners compared to the trained Harman listeners. Performance is based on the group’s average loudspeaker F-statistic which represents their ability to give discriminating and consistent preference ratings.|
Are There Cross-Cultural Preferences in Sound Quality?
One of the oldest controversies in audio is the notion that different cultures or geographical regions of the world have different sound quality preferences [see reference 2]. For example, it is often claimed that Japanese listeners have different loudspeaker preferences than Americans due to differences in language, music, cultural practices and norms, and the acoustics of their homes. So far, very little formal research has done on this subject. In some preliminary studies, the author has found no significant differences in sound quality preferences for loudspeakers and automotive audio systems among Chinese, Japanese and American listeners.
How to Listen: A New Listener Training Software Application
Research has found most sound quality percepts fall under the attribute categories of timbre, spatial, dynamic or related nonlinear distortion. Within these four attributes there are additional sub-attributes that describe more specific sonic characteristics of the attribute. For example, Bright-Dull and Full-Thin are timbre sub-attributes related to the relative emphasis and de-emphasis of high and low frequencies, respectively. Sub-attributes for spatial quality deal with the location and width of the auditory image(s), and the perceived sense of spaciousness or envelopment. Distortion sub-attributes include the presence of noise, hum, audible clipping and distortions specific to the audio device(s) under test.
How to Listen focuses on teaching listeners to evaluate sound quality differences based on these four attributes and their sub-attributes (see Figure 4). While listening to music recordings, one or more attributes are manipulated in a controlled way so that listeners recognize and report the magnitude of these changes using the appropriate terms and scales. An analogy to this would the Wine Aroma Wheel where expert wine tasters are trained to identify the intensities of different aroma-flavors perceived in the wine.
|Figure 4: A list of the 17 different training tasks that focus on one or more of the four sound quality attributes: spectral (timbral), spatial, distortion and dynamics.|
To facilitate the training process, a proprietary computer-based software program called “How to Listen” was developed by Harman software engineers Sean Hess and Eric Hu. The software runs on both Mac and PC computers, and can play both stereo and multichannel music files. A real-time DSP engine built into the software application allows real-time manipulation of sound quality attributes in response to the listeners’ responses and performance.
There are currently five different types of training tasks that focus on one or more sound quality attributes (see Figure 4):
- Band Identification
- Spectral Plot
- Spatial Mapping
- Attribute Test
- Preference Test