Harman's "How to Listen" - A New Computer-based Listener Training Program

Trained listeners with normal hearing are used at Harman International for all standard listening tests related to research and competitive benchmarking of consumer, professional and automotive audio products. This article explains why we use trained listeners, and describes a new computer-based software program developed for training and selecting Harman listeners.

Why Train Listeners?

There are many compelling reasons for training listeners. First, trained listeners produce more discriminating and reliable judgments of sound quality than untrained listeners [1]. This means that fewer listeners are needed to achieve the same statistical confidence, resulting in considerable cost savings. Second, trained listeners are taught to identify, classify and rate important sound quality attributes using precise, well-defined terms to explain their preferences for certain audio systems and products. Vague audiophile terms such as “chocolaty”, “silky” or “the bass lacks pace, rhythm or musicality” are NOT part of the trained listener's vocabulary since these descriptors are not easily interpreted by audio engineers who must use the feedback from the listening tests to improve the product. Third, the Harman training itself, so far, has produced no apparent bias when comparing the loudspeaker preferences of trained versus untrained listeners [1]. This allows us to safely extrapolate the preferences of trained listeners to those of the general untrained population of listeners (e.g. most consumers).

Harman's “How to Listen” Listener Training Program

Harman’s “How to Listen” is a new computer-based software application that helps Harman scientists efficiently train and select listeners used for psychoacoustic research and product evaluation. The self-administered program has 17 different training tasks that focus on four different attributes of sound quality: timbre (spectral effects), spatial attributes(localization and auditory imagery characteristics), dynamics, and nonlinear distortion artifacts. Each training task starts at a novice level, and gradually advances in difficulty based on the listeners’ performance. Constant feedback on the listener's responses is provided to improve their learning and performance. A presentation of the training software can be viewed in parts 1 and 2

Spectral Training Tasks

There are two different spectral training tasks. In the Band Identification training task, the listener compares a reference (Flat) and an equalized version of the music program (EQ), and must determine which frequency band is affected by the equalization (see slide 5 of part 2). The types of filters include peaks, dips, peak and dips, high/low shelving and low/high/bandpass filters. The task is aimed at teaching listeners to identify spectral distortions in precise, quantitative terms (filter type, frequency, Q and gain) that directly correspond to a frequency response measurement.

At the easiest skill level, there are only 2 frequency band choices, which are easily detected and classified. However, as the listener advances, the audio bandwidth is subdivided into multiple frequency bands making the audibility and identification of the affected frequency band more challenging.

The Spectral Plot training exercise takes this one step further. The listener compares different music selections equalized to simulate more complex frequency response shapes commonly found in measurements of loudspeakers in rooms and automotive spaces. The listener is given a choice of frequency curves which they must correctly match to the perceived spectral balances of the stimuli. This teaches listeners to graphically draw the perceived timbre of an audio component as a frequency response curve. Once trained, listeners become quite adept at drawing the perceived spectral balance of different loudspeakers, and these graphs closely correspond to their acoustical measurements [2], [3].

Sound Quality Attribute Tasks

The purpose of this task is to familiarize the listener with each of the four sound quality attributes (timbre, spatial, dynamics and nonlinear distortion) and their sub-attributes, and measure the listener's ability to reliably rate differences in the attribute's intensity. For example, in one task the listener must rank order the relative brightness/dullness of two or more stimuli based on the intensities of the brightness/dullness of the processed music selection. As the difficulty of the task increases, the listener must rate more stimuli that have incrementally smaller differences in intensity of the tested attribute. Listener performance is calculated using Spearman’s rank correlation coefficient which expresses the degree to which stimuli have been correctly rank ordered on the attribute scale.

Preference Training

In this task, the listener enters preference ratings for different music selections that have had one or more attributes (timbre, spatial, dynamics and nonlinear distortion) modified by incremental amounts.

By studying the interrelationship between the modification of these attributes and the preference ratings, Harman scientists can uncover how listeners weight different attributes when formulating their preferences. From this, the preference profile of a listener can be mapped based on the importance they place on certain sound quality attributes. The performance metric in the preference task is based on the F-statistic calculated from an ANOVA performed on the individual listeners’ data. The higher the F-statistic, the more discriminating and/or consistent the listeners’ ratings are --- a highly desirable trait in the selection of a listener.

Other Key Features

Harman’s “How to Listen” training software runs on both Windows and Mac OSX platforms, and includes a real-time DSP engine for manipulating the various sound quality attributes. Most common stereo and multichannel sound formats are supported. In “Practice Mode”, the user can easily add their own music selections.

All of the training results from the 100+ listeners located at Harman locations world-wide are stored on a centralized database server. A web-based front end will allow listeners to log in to monitor and compare their performances to those of other listeners currently in training. Of course, the identifies of the other listeners always remain confidential.

Conclusion

In summary, Harman’s “How to Listen” is a new computer-based, self-guided software program that teaches listeners how to identify, classify and rate the quality of recorded and reproduced sounds according to their timbral, spatial, dynamic and nonlinear distortion attributes. The training program gives constant performance feedback and analytics that allow the software to adapt to the ability of the listener. These performance metrics are used for selecting the most discriminating and reliable listeners used for research and subjective testing of Harman audio products.

References

[1] Sean. E Olive, "Differences in Performance and Preference of Trained Versus Untrained Listeners in Loudspeaker Tests: A Case Study," J. AES, Vol. 51, issue 9, pp. 806-825, September 2003. Download for free here, courtesy of Harman International.

[2] Sean E. Olive, “A Multiple Regression Model for Predicting Loudspeaker Preference Using Objective Measurements: Part I - Listening Test Results,” presented at the 116th AES Convention (May 2004).

[3] Floyd E. Toole, Sound Reproduction: The Acoustics and Psychoacoustics of Loudspeakers and Rooms, Focal press (July 2008). Available from Amazon here