Understanding Sound Masking

A sound masking system uses a series of electronic components and loudspeakers to distribute a sound similar to softly blowing air, causing many occupants to presume HVAC is its source. However, unlike HVAC, this sound is continuous and precisely controllable.

Though masking technology is often referred to by the term ‘white noise,’ modern systems do not utilize a particular color of sound. Rather, they are engineered so their output can be tuned post-installation in order to meet a spectrum or ‘curve’ specifically optimized for comfort and masking of speech.

The premise behind this solution is simple: our ability to discern a sound (e.g., noise or speech) is reduced when its level falls below that of the masking sound level. Moreover, the disruptive impact of sounds that remain above the controlled background sound level is lessened due to the reduction in the degree of change between the baseline and volume peaks. Consequently, occupants perceive treated spaces as quieter.

There are many everyday examples of this effect, such as running water, rustling leaves or the murmur inside a busy restaurant. However, when introducing a masking sound to a workplace, it is vital to ensure that it is also as unobtrusive as possible, making post-installation tuning an essential part of the commissioning process within each facility.

Overcoming Preconceptions

Considering that sound masking has been available since the late 1960s, one might wonder why the building community has yet to embrace it as the foundation for acoustical design. To understand this delay, one has to consider the technology’s history.

Sound masking systems were first adopted to help with the acoustical challenges encountered in an ever-growing number of open plans. This initial application led some to conclude that masking was only intended for these types of areas.

This opinion was also reinforced by a significant technical impediment. Early sound masking systems were designed using centralized architecture, which is very limited in terms of its ability to offer local control over the masking sound because they consisted of large zones that spanned numerous private offices and other closed rooms. This strategy fails to recognize the impact the space’s architectural features (size, geometry, furnishings, finishings, etc.) have on the sound being delivered to the space and, hence, provides little to no opportunity to address it via local control over level and frequency. The resulting inconsistencies in masking performance led vendors and dissatisfied customers to conclude that the technology could not be applied in closed spaces.

Modern networked masking architecture addresses these historical objections by providing fine control over both level and frequency within small zones (i.e., one zone per closed office, and adjustment zones no larger than three loudspeakers, or 675 square feet [62.7 m2], within an open plan), but some still argue that closed rooms do not require sound masking because they are afforded sufficient speech privacy and noise control via physical isolation. By the same token, when a closed room fails to provide these attributes for its occupants, it is typically blamed on deficiencies in its design, construction, and/or maintenance.

Cracks in the Armor

While they might be a contributing factor, this failure cannot solely be attributed to cracks in the walls’ armor because speech privacy is not determined by isolation alone. A person’s ability to clearly understand a conversation is dependent on two variables: the received level of the speaker’s voice and the background sound level in the listener’s location. The relationship between the two is called the signal-to-noise ratio.

Traditional closed room construction attempts to provide privacy by simply reducing the signal. If a solution has not been implemented to control the minimum background sound level in adjoining areas and it is lower than the sounds passing through the wall or via various flanking paths—gaps along the window mullions, ceiling and floors, as well as through the plenum, ductwork, return air grills, and open doors—conversations and noises will still be audible and potentially intelligible.

Regardless, unless a sound masking system is implemented—as well as professionally tuned, and its performance verified post-installation—the minimum background sound level is not a known quantity. HVAC and other mechanical systems are sometimes thought to provide masking, but as noted above, one cannot reasonably expect this type of equipment to deliver a consistent level over time/space or to generate a spectrum conducive to speech privacy.

Accordingly, ASTM E1374-18e1, Standard Guide for Open Office Acoustics and Applicable ASTM Standards was revised. The discussion of HVAC noise in the previous iteration, ASTM E1374-18, Standard Guide for Office Acoustics and Applicable ASTM Standards, pertains only to limiting maximum noise levels rather than using this equipment for masking. Further, a sound masking system is identified as the only viable source of a continuous minimum background sound level. As the title change suggests, this standard’s scope has also been broadened; it now applies to private offices and conference rooms, not only to open plan.

With the advent of computer-tuned masking systems, a minimum background sound level is now a readily deliverable component of architectural acoustic design. Using it—even to apply a level as low as the 30 dBA on which recommended STC ratings are often based—allows the expected degree of speech privacy to be more reliably achieved.

Building professionals can use this predictable background sound level as the foundation for the remainder of their acoustical plan, allowing more accurate selection of the blocking and absorptive elements—and providing a means of reducing the specifications for the room’s physical shell, while still achieving the desired level of speech privacy.

Calculating the Benefits

But is an equal or greater level of privacy achievable using this alternative?

One method of resolving this question is to use speech privacy metrics, which allow one to objectively assess the impact of increased attenuation and background sound level on intelligibility. The grandfather of intelligibility metrics is the Articulation Index (AI), defined in ASTM E1130-16, Standard Test Method for Objective Measurement of Speech Privacy in Open Plan Spaces Using Articulation Index. The calculation methodology for the AI accounts for both aforementioned parameters. And although the standard’s title references open plan spaces, it was originally derived, and used, for enclosed spaces as well. That said, some acousticians prefer to use SPC—derived from the same theory as the AI—as defined in ASTM E2638-10, Standard Test Method for Objective Measurement of the Speech Privacy Provided by a Closed Room to predict “higher levels of speech privacy” or speech security (i.e., when speech tends towards becoming inaudible) for closed spaces. SPC is derived from the same theory as the AI.

Calculation of AI is based on several measurements taken in the space in question, as well as a standardized normal voice level. Onsite testing determines the amount by which voice level is reduced between the source room and the listener location. The difference between the voice level and the background in each of the third-octave frequency bands (200 to 5000 Hz) provides the signal-to-noise ratio in the listener location. Because certain frequencies contribute more significantly to intelligibility than others, the AI method assigns a specific weighting formula to determine an AI contribution within each frequency band, and these are summed to arrive at the AI value. By contrast, the calculation methodology used for the SPC does not discriminate between frequencies.

Using this method, one can quantify the impact of increasing the attenuation of the wall and that of increasing the masking level, allowing comparison of the two strategies. Obviously, as wall attenuation increases, for each decibel reduction there is an increase in speech privacy levels.

Mathematically, the same can be achieved by raising the background sound level by a decibel. To understand why, one need only look to the step in the above AI calculation that determines the signal-to-noise ratio. If a wall decreases the intrusion of voice into the room by a decibel, then the signal-to-noise ratio is reduced by a point. An identical drop occurs when the masking level is raised by one decibel.

Masking typically adds approximately 5 to 12 dBA of ambient sound to closed rooms.