Dynamic Range Compression and Noise

This post accompanies our presentation “Dynamic Range Compression of Sound Mixtures” at the 2020 Acoustical Society of America meeting. Complete details are available in the paper preprint “Modeling the effects of dynamic range compression on signals in noise.”

Nearly every modern hearing aid uses an algorithm called dynamic range compression (DRC), which automatically adjusts the amplification of the hearing aid to make quiet sounds louder and loud sounds quieter. Although compression is one of the most important features of hearing aids, it might also be one of the reasons that they work so poorly in noisy environments. Hearing researchers have long known that when DRC is applied to multiple sounds at once, it can cause distortion and make background noise worse. Our research team is applying signal processing theory to understand why compression works poorly in noise and exploring new strategies for controlling loudness in noisy environments.

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Face masks make it harder to hear, but amplification can help

This post describes our recent paper “Acoustic effects of medical, cloth, and transparent face masks on speech signals” in The Journal of the Acoustical Society of America. This work was also discussed in The Hearing Journal and presented at the 179th Acoustical Society of America meeting.

Face masks are a critical tool in slowing the spread of COVID-19, but they also make communication more difficult, especially for people with hearing loss. Face masks muffle high-frequency speech sounds and block visual cues. Masks may be especially frustrating for teachers, who will be expected to wear them while teaching in person this fall. Fortunately, not all masks affect sound in the same way, and some masks are better than others. Our research team measured several face masks in the Illinois Augmented Listening Laboratory to find out which are the best for sound transmission, and to see whether amplification technology can help.

Several months into the pandemic, we now have access to a wide variety of face masks, including disposable medical masks and washable cloth masks in different shapes and fabrics. A recent trend is cloth masks with clear windows, which let listeners see the talker’s lips and facial expressions. In this study, we tested a surgical mask, N95 and KN95 respirators, several types of opaque cloth mask, two cloth masks with clear windows, and a plastic face shield.

Face masks ranked by high-frequency attenuation

We measured the masks in two ways. First, we used a head-shaped loudspeaker designed by recent Industrial Design graduate Uriah Jones to measure sound transmission through the masks. A microphone was placed at head height about six feet away, to simulate a “social-distancing” listener. The loudspeaker was rotated on a turntable to measure the directional effects of the mask. Second, we recorded a human talker wearing each mask, which provides more realistic but less consistent data. The human talker wore extra microphones on his lapel, cheek, forehead, and in front of his mouth to test the effects of masks on sound capture systems.

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What do you want from hearing tech?

Our team at the Illinois Augmented Listening Laboratory is developing technologies that we hope will change the way that people hear. But the technology is only one half of the story. If we want our research to make a difference in people’s lives, we have to talk to the people who will use that technology.

Our research group is participating in the National Science Foundation Innovation Corps, a technology translation program designed to get researchers out of the laboratory to talk to real people. By understanding the needs of the people who will benefit from our research, we can make sure we’re studying the right problems and developing technology that will actually be used. We want to hear from:

  • People with hearing loss who use hearing aids and assistive listening devices
  • People who don’t use hearing technology but sometimes have trouble hearing
  • Parents, teachers, school administrators, and others who work with students with hearing loss
  • Hearing health professionals
  • People who work in the hearing technology industry

This is not a research study: there are no surveys, tests, or consent forms. We want to have a brief, open-ended conversation about your needs, the technology that you use now, and what you want from future hearing technology.

To schedule a call with our team, please reach out to Ryan Corey (corey1@illinois.edu). Most calls last about 15 minutes and take place over video, though we’re happy to work around your communication needs.

Hearing aid algorithm adapted for COVID-19 ventilators

Audio signal processing would seem to have nothing to do with the COVID-19 pandemic. It turns out, however, that a low-complexity signal processing algorithm used in hearing aids can also be used to monitor breathing for patients on certain types of ventilator.

To address the shortage of emergency ventilators caused by the pandemic, this spring the Grainger College of Engineering launched the Illinois RapidVent project to design an emergency ventilator that could be rapidly and inexpensively produced. In little more than a week, the team built a functional pressure-cycled pneumatic ventilator, which is now being manufactured by Belkin.

The Illinois RapidVent is powered by pressurized gas and has no electronic components, making it easy to produce and to use. However, it lacks many of the monitoring features found in advanced commercial ventilators. Without an alarm to indicate malfunctions, clinicians must constantly watch patients to make sure that they are still breathing. More-advanced ventilators also display information about pressure, respiratory rate, and air volume that can inform care decisions.

The Illinois RapidAlarm adds monitoring features to pressure-cycled ventilators.

To complement the ventilator, a team of electrical engineers worked with medical experts to design a sensor and alarm system known as the Illinois RapidAlarm. The device attaches to a pressure-cycled ventilator, such as the Illinois RapidVent, and monitors the breathing cycle. The device includes a pressure sensor, a microcontroller, a buzzer, three buttons, and a display. It shows clinically useful metrics and sounds an audible alarm when the ventilator stops working. The hardware design, firmware code, and documentation are available online with open-source licenses. A paper describing how the system works is available on arXiv.

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Audio Source Remixing

This post describes our paper “Binaural Audio Source Remixing for Microphone Array Listening Devices” presented at ICASSP 2020. You can read the paper here or watch the video presentation here.

Microphone arrays are important tools for spatial sound processing. Traditionally, most methods for spatial sound capture can be classified as either beamforming, which tries to isolate a sound coming from a single direction, or source separation, which tries to split a recording of several sounds into its component parts. Beamforming and source separation are useful in crowded, noisy environments with many sound sources, and are widely used in speech recognition systems and teleconferencing systems.

Since microphone arrays are so useful in noisy environments, we would expect them to work well in hearing aids and other augmented listening applications. Researchers have been building microphone-array hearing aids for more than 30 years, and laboratory experiments have consistently shown that they can reduce noise and improve intelligibility, but there has never been a commercially successful listening device with a powerful microphone array. Why not?

The problem may be that most researchers have approached listening devices as if they were speech recognition or teleconferencing systems, designing beamformers that try to isolate a single sound and remove all the others. They promise to let the listener hear the person across from them in a crowded restaurant and silence everyone else. But unlike computers, humans are used to hearing multiple sounds at once, and our brains can do a good job separating sound sources on their own. Imagine seeing everyone’s lips move but not hearing any sound! If a listening device tries to focus on only one sound, it can seem unnatural to the listener and introduce distortion that makes it harder, not easier, to hear.

A source remixing system changes the relative levels of sounds in a mixture while preserving their spatial cues.

This paper proposes a new type of array processing for listening devices: source remixing. Instead of trying to isolate or separate sound sources, the system tries to change their relative levels in a way that sounds natural to the listener. In a good remixing system, it will seem as if real-world sounds are louder or quieter than before.

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Deformable Microphone Arrays

This post describes our paper “Motion-Robust Beamforming for Deformable Microphone Arrays,” which won the best student paper award at WASPAA 2019.

When our team designs wearable microphone arrays, we usually test them on our beloved mannequin test subject, Mike A. Ray. With Mike’s help, we’ve shown that large wearable microphone arrays can perform much better than conventional earpieces and headsets for augmented listening applications, such as noise reduction in hearing aids. Mannequin experiments are useful because, unlike a human, Mike doesn’t need to be paid, doesn’t need to sign any paperwork, and doesn’t mind having things duct-taped to his head. There is one major difference between mannequin and human subjects, however: humans move. In our recent paper at WASPAA 2019, which won a best student paper award, we described the effects of this motion on microphone arrays and proposed several ways to address it.

Beamformers, which use spatial information to separate and enhance sounds from different directions, rely on precise distances between microphones. (We don’t actually measure those distances directly; we measure relative time delays between signals at the different microphones, which depend on distances.) When a human user turns their head – as humans do constantly and subconsciously while listening – the microphones near the ears move relative to the microphones on the lower body. The distances between microphones therefore change frequently.

In a deformable microphone array, microphones can move relative to each other.

Microphone array researchers have studied motion before, but it is usually the sound source that moves relative to the entire array. For example, a talker might walk around the room. That problem, while challenging, is easier to deal with: we just need to track the direction of the user. Deformation of the array itself – that is, relative motion between microphones – is more difficult because there are more moving parts and the changing shape of the array has complicated effects on the signals. In this paper, we mathematically analyzed the effects of deformation on beamformer performance and considered several ways to compensate for it.

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Cooperative Listening Devices

This post describes our paper presented at CAMSAP 2019.

Imagine what it would sound like to listen through someone else’s ears. I don’t mean that in a metaphorical sense. What if you had a headset that would let you listen through microphones in the ears of someone else in the room, so that you can hear what they hear? Better yet, what if your headset was connected to the ears of everyone else in the room? In our group’s latest paper, “Cooperative Audio Source Separation and Enhancement Using Distributed Microphone Arrays and Wearable Devices,” presented this week at CAMSAP 2019, we designed a system to do just that.

Our team is trying to improve the performance of hearing aids and other augmented listening devices in crowded, noisy environments. In spaces like restaurants and bars where there are many people talking at once, it can be difficult for even normal-hearing people to hold a conversation. Microphone arrays can help by spatially separating sounds, so that each user can hear what they want to hear and turn off the sounds they don’t want to hear. To do that in a very noisy room, however, we need a large number of microphones that cover a large area.

Illustration of human listeners with headsets, microphone-array appliances, and sound sources spread around a room

Complex listening environments include many different sound sources, but also many microphone-equipped devices. Each listening device tries to enhance a different sound source.

In the past, we’ve built large wearable microphone arrays with sensors that cover wearable accessories or even the entire body. These arrays can perform much better than conventional earpieces, but they aren’t enough in the most challenging environments. In a large, reverberant room packed with noisy people, we need microphones spread all over the room. Instead of having a compact microphone array surrounded by sound sources, we should have microphones spread around and among the sound sources, helping each listener to distinguish even faraway sounds.

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Massive Distributed Microphone Array Dataset

This post describes our new massive distributed microphone array dataset, which is available for download from the Illinois Databank and is featured an upcoming paper at CAMSAP 2019.

Conference room with microphone arrays and loudspeakers

The conference room used for the massive distributed array dataset.

Listening in loud noise is hard: we only have two ears, after all, but a crowded party might have dozens or even hundreds of people talking at once. Our ears are hopelessly outnumbered! Augmented listening devices, however, are not limited by physiology: they could use hundreds of microphones spread all across a room to make sense of the jumble of sounds.

Our world is already filled with microphones. There are multiple microphones in every smartphone, laptop, smart speaker, conferencing system, and hearing aid. As microphone technology and wireless networks improve, it will be possible to place hundreds of microphones throughout crowded spaces to help us hear better. Massive-scale distributed arrays are more useful than compact arrays because they are spread around and among the sound sources. One user’s listening device might have trouble distinguishing between two voices on the other side of the room, but wearable microphones on those talkers can provide excellent information about their speech signals.

Many researchers, including our team, are developing algorithms that can harness  information from massive-scale arrays, but there is little publicly available data suitable for source separation and audio enhancement research at such a large scale. To facilitate this research, we have released a new dataset with 10 speech sources and 160 microphones in a large, reverberant conference room.

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