Popular version of 3pPA4 – Estimation of seabed properties at the New England Mud Patch using vector acoustic measurements
Presented at the 190th ASA Meeting
Read the abstract at https://eppro01.ativ.me/web/planner.php?id=ASASPRING2026
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Shear is one of the fundamental mechanical parameter that bridges geological, engineering, and environmental aspects of the seafloor influencing loss of acoustic energy in addition to other factors such as seafloor stability, load bearing capacity, sediment transport and deposition. Shear wave velocity is one of the parameters which characterizes shear strength of the sediments. In this study we use waves propagating along the seabed (interface waves) to estimate the shear speed of the sediments.
Interface waves:
Interface waves are waves which travel along an interface between two media. Examples include Rayleigh waves (waves which travel along land) and Scholte waves (waves along seabed). Figure 1 shows a typical scenario in which a sensor on the seabed will measure Scholte waves in addition to acoustic waves along different paths (direct, surface reflected etc.).
Fig. 1: Schematic of a typical scenario in which a sensor on the seabed measures interface waves in addition to acoustic waves along different paths. Right panel shows the OBX sensor package.
The Scholte waves have the following characteristics:
They have maximum amplitude at the water-sediment interface (seabed). The data used in this study is from a receiver deployed on the seabed.
Particles in the medium traces an elliptical path in water and sediment.
The magnitude of the particle motion decreases exponentially as a function of distance from the interface in both media.
The ratio of the horizontal to vertical component of the particle motion is strongly correlated to the shear velocity and thickness of the sediment. In this study we have used this characteristics of the Scholte wave to estimate the shear velocity in the sediment.
We measured the particle velocities along three mutually orthogonal directions associated with Scholte waves using a senor package (Ocean Bottom Recorder or OBX, shown in the right panel of Figure 1) deployed on the seabed during an experiment in 2022 in the New England Mud patch (NEMP), 200 km south of Martha’s Vineyard in 70 m of water depth. As the name implies, NEMP has a layer of mud/clay sediments on top of sand. Many types of sources generated sound at different frequency bands in addition to sources of opportunity such as ships passing close to the experimental area. Figure 2 shows an example of the motion (velocity in mm/s) of the particle measured by the OBX during the experiment. This represents the motion of the particle for a short period of time (~ 1 seconds) in a narrow frequency band.
Fig.2: The trace of the particle motion (hodogram) in the source-to-receiver direction (radial, shown in pink), in the vertical direction (normal to the seabed, shown in yellow). The red curve shows the path of the particle in the vertical plane containing the source and receiver.
The strong correlation of horizontal to vertical ratio (HVSR) of the particle motion to shear speed in the sediment and sediment layer thickness is demonstrated using simulated data in Figure 3. Particle motion data were simulated for a ocean environment as shown in the left panel of Figure 3. Sound speeds in the water column, sediment and basement were assumed as 1500 m/s, 1495 m/s and 1750 m/s respectively. The shear speeds in the sediment and basement were assumed as 50 m/s and 300 m/s respectively. Densities in the water column, sediment and basement were assumed as 1025 kg/m3, 1650 kg/m3 and 2000 kg/m3 respectively.
Fig.3: Ratio of the horizontal to vertical (HVSR) particle motion amplitude as a function of frequency (right panel). Particle motion was simulated for an ocean environment as shown in the left panel.
The particle velocities of the Scholte waves for this environment were generated using a numerical model and ratio of the horizontal to vertical component of the particle motion amplitudes were calculated as a function of frequency (Figure 3; right panel). The HVSR curve shows a dominant peak at 2 Hz which correspond to the shear resonant frequency. The data measured in the NEMP experiment is used to calculate the HVSR and then identify the peak in the frequency versus HVSR curve. HVSR is then modelled for various shear speeds and layer thicknesses. The shear speed which produces the best data-model match (particularly the peak frequency) is then estimated.
Acentech, 33 Moulton St., Cambridge, MA, 02138, United States
Popular version of 2aAAb1 – Signal-to-noise ratio in restaurants: fine lines between terrific and terrible dining experiences
Presented at the 190th ASA Meeting
Read the abstract at https://eppro01.ativ.me/web/planner.php?id=ASASPRING2026
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Restaurant noise is one of the most common acoustical complaints. Since a truly quiet restaurant is an empty one, the question becomes, “how much are diners willing to cope with noise as a trade-off for good food and a fun night out?”.
Crowdsourced data from the app SoundPrint, which has logged more than 100,000 noise measurements in restaurants, suggests that the difference between a restaurant where conversation feels easy and one where it feels exhausting is relatively small: only about 7 decibels (dB), which is about the difference between a raised and normal speaking voice. And the average noise level of a “quiet” restaurant? Approximately 70 dBA. The dBA unit indicates that the noise was adjusted to approximate the human ear’s response to sound.
Figure 1 – Soundprint User Interface. Customers measure fifteen seconds of noise and then rate their ability to have a conversation (courtesy of SoundPrint).
This might seem very loud if you’ve ever used a sound level meter app on your phone. Because it is – our indoor environments are purposefully designed to be 45 dBA or much quieter to provide comfort, rest, and the right environment for mentally demanding tasks like business conference calls or listening to class lectures.
Restaurants test the resilience of our hearing system and require a combination of visual and auditory cues to fill in the gaps. Whether or not we can clearly see the person’s face, pick up context clues from the conversation, or know the person’s voice all play a role in helping us understand each other in restaurants. If you are about to have a business dinner with someone you’ve never met before, think twice about where you are going and pull up a few photos. Does the ceiling look shiny and seamless? Are the tables close together? Is it mostly mood lighting with table lamps? If so, you might not land that deal.
Figure 2 – A charming restaurant that serves delicious food; however, guests may have a hard time hearing each other during peak hours.
Restaurant noise is not guesswork. Acoustical consultants can study architectural drawings, occupancy counts, and room finish materials to determine noise levels in restaurants based on the number of customers. If restaurant owners want to fix the issue, the optimal solution often must accommodate tight operating margins: find the cost-effective, minimally invasive solution.
The cacophony of restaurants is often simple oversight: the designer did not hire an acoustical consultant and opted for a sound-reflective ceiling material instead of a sound-absorbing ceiling. The reason this remains a consistent issue even after the restaurant has opened is far more complex: a noisy packed dining room suggests a thriving restaurant, creates turnover, and a tight table arrangement means more paying customers. Restaurants are a business and noisemakers are the clients. Add a thumping soundtrack to the mix and you have a perfect recipe for a strained voice and splitting headache.
A quiet restaurant is an empty one. However, owners, designers, and consultants can establish realistic goals if we reference crowdsourced data and reframe what quiet means in the context of restaurants. Practical advice from acoustical consultants can make all the difference between a terrible and acceptable dining experience, increasing the chance that customers come back.
If it is any solace, restaurants have been obnoxiously loud for nearly a century (see Figure 3).
Figure 3: Noise levels found at home and in restaurants. Adapted from “Present Methods of Sound Measurement” by A.H. Davis, (Architects’ Journal, May 1938)
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Some Arctic animals don’t need to see ice to find it—they can hear it. Species like the beluga whale use sound to navigate through icy waters where visibility is limited, finding breathing holes in the ice without ever seeing them. This project asks a simple question: Can a computer learn to do the same? By analyzing acoustic signals, we show that a neural network can detect ice without relying on visual information.
Initial experiments were conducted in a laboratory tank (Figure 1) at the Brigham Young University Department of Physics and Astronomy. We took sound recordings when ice was and was not present on the surface of the water. Then, we trained a machine learning classifier to label the recordings as ‘ice’ or ‘no ice.’
Figure 1. Laboratory tank (side view).
For these experiments, we placed an underwater loudspeaker (transmitter) and an underwater microphone (hydrophone) in the tank. The transmitter produced ultrasonic chirps of increasing frequency when ice was and wasn’t present. We added about 600 pounds of block ice to the tank and took one-second recordings before ice was added, while it was present, and after it melted. We took two additional sets of recordings for testing the neural network: one using block ice and one using pebble ice.
After we acquired the recordings, we needed to label them. We did this using camera footage of the tank (Figure 2). Recordings with about 5% or more ice coverage between the transmitter and the hydrophone were labeled ‘ice,’ and recordings with less than 5% coverage were labeled ‘no ice.’ We chose this 5% threshold to differentiate between negligible and non-negligible ice cover. We converted each labeled recording into a time-frequency spectrogram and used the spectrograms to train a machine learning classifier.
Figure 2. Camera footage of the laboratory tank for labeling.
For the machine learning classifier, we selected a convolutional neural network (CNN) because it can detect important features indicating the presence of ice. We passed the spectrograms and their associated labels through the classifier for training, where the CNN learned to associate certain features of spectrograms with their labels. Ten classifiers were trained to provide a statistical representation of performance.
Figure 3. Roadmap of how each audio recording was processed and classified.
Once the ten classifiers were trained, we tested their performance on two other datasets that they were not trained on. We did this to see how well the CNN could generalize to other conditions. This generalizability is important because, in practical applications, the ocean environment is always changing: no two recordings will ever have identical conditions. The mean labeling accuracy across the ten classifiers on the testing block ice dataset was 93.5% ± 0.9%. On the pebble ice dataset, the classifiers achieved 94.3% ± 1.4% accuracy. These tests show that the CNNs can generalize well to new conditions.
The high accuracy of these initial experiments indicates that a CNN can use sound to detect the presence of ice. Just as the beluga whale listens for audio cues to find breathing holes in the ice, the neural network extracts important information from the sound to determine whether ice is present.
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
If you listen to two different sounds that are similar in pitch across the ears, something strange happens. The two sounds blend perceptually to create an illusion of a new sound, similar to what happens with different colors across the eyes.
For example, if you listen here with stereo headphones to the vowels “ah” as in hot and “ee” as in heed, spoken by two different talkers with different voice pitch – a male talker and a female talker – you will hear two vowels.
Figure 1. Perception when two different vowels are played to the two ears at different pitch. Play different pitch example.Note: Stereo headphones are necessary to experience the illusion
But if these same vowels are spoken by the same talker, you will experience something called binaural fusion (Reiss and Molis, 2021). Instead of hearing two different vowels, you will hear a single new vowel. This new vowel will be a blend of the two original vowels, something in between like “eh” as in head.
Figure 2. Perception when two different vowels are played to the two ears at the same pitch. Play same pitch example. Note: Stereo headphones are necessary to experience the illusion
This illusion is not confined to steady sounds, but also happens for sounds that are fluctuating, such as a tone that is fluctuating in one ear and steady in the other ear. This makes localization of the fluctuating tone difficult.
While we know that people experience binaural fusion, we don’t know what happens in the brain so that some sounds fuse while others are heard as distinct. It’s hard to measure detailed brain activity in humans, so we are now studying what happens in the brain of animals, in this case ferrets, when they experience the same illusion. The first thing we had to do was demonstrate that ferrets perceive these illusions the same way as humans. For vowels, ferrets were first trained to indicate when they heard the vowel “eh”, and to ignore the vowels “ah” and “ee”. When “ah” and “ee” were played to the two ears at the same pitch, the ferrets responded that they heard “eh”. Similarly, for fluctuating tones, ferrets were trained to indicate the side where they heard the fluctuating tone, and they experienced the same difficulties as human listeners.
As a next step, recordings from cells in the brain will reveal how brain activity leads to these illusory phenomena. Binaural fusion and the converse, binaural fission, are important to understand because together they underlie how the brain groups components of sound that belong to one source, such as a single talker, and separates those that belong to different sources, such as other talkers (Bregman, 1990; Bronkhorst, 2000).
It is shown that people with hearing loss, including those with cochlear implants, often experience excessive binaural fusion, and fuse voices of different pitch together (Reiss et al., 2014; 2017; 2018). Excessive binaural fusion explains a large portion of difficulties with understanding speech in noisy environments (Oh et al., 2022; 2023). Understanding how brain circuits encode binaural fusion and fission will show us how to train or rewire the brain to help people with hearing loss and other auditory processing disorders.
In the meantime, think about how you can come up with other new illusory sounds by combining two different sounds of the same pitch!
Works cited
Bregman, A. S. (1990). Auditory Scene Analysis (MIT Press, Cambridge, MA).
Bronkhorst, A. W. (2000). The cocktail party phenomenon: A review of research on speech intelligibility in multiple-talker conditions. Acta Acustica united with Acustica, 86(1), 117-128.
Oh, Y., Hartling, C. L., Srinivasan, N. K., Diedesch, A. C., Gallun, F. J., & Reiss, L. A. J. (2022). Factors underlying masking release by voice-gender differences and spatial separation cues in multi-talker listening environments in listeners with and without hearing loss. Frontiers in neuroscience, 16, 1059639.
Oh, Y., Srinivasan, N.K., Hartling, C.L., Gallun, F.J., and Reiss, L.A.J. (2023). Differential effects of binaural pitch fusion range on the benefits of voice gender differences in a ‘cocktail party’ environment for bimodal and bilateral cochlear implant users. Ear Hear. 44(2), 318–329.
Reiss, L. A., Fowler, J. R., Hartling, C. L., and Oh, Y. (2018) Binaural pitch fusion in bilateral cochlear implant users. Ear Hear. 39(2), 390-397.
Reiss, L.A., Ito, R.A., Eggleston, J.L., and Wozny, D.R. (2014). Abnormal binaural spectral integration in cochlear implant users. J. Assoc. Res. Otolaryngol., 15(2), 235–248.
Reiss, L.A.J., and Molis, M.R.. (2021) An Alternative Explanation for Difficulties with Speech in Background Talkers: Abnormal Fusion of Vowels across Fundamental Frequency and Ears. J. Assoc. Res. Otolaryngol., 22(4): 443-461.
Reiss, L.A., Shayman, C.S., Walker, E.P., Bennett, K.O., Fowler, J.R., Hartling, C.L., Glickman, B., Lasarev, M.R., and Oh, Y. (2017). Binaural pitch fusion: Comparison of normal-hearing and hearing-impaired listeners. J.Acoust. Soc. Am., 141(3), 1909–1920.
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Hearing aids help millions of people hear speech more clearly. But they may quietly reshape something else: your sense of where sounds are coming from. A new wave of affordable, over-the-counter (OTC) hearing aids is now available and they come in a wide variety of shapes and sizes and styles. Our study aims to understand what characteristics of hearing aids support (or disrupt) sound localization.
The ability to localize sound (knowing whether a car is approaching from the left or right, whether a voice is coming from in front of you or behind) is something most people take for granted. This spatial awareness relies on subtle acoustic cues available at the two ears. These cues can easily be disrupted by devices placed in or around the ear. Listeners with mild hearing loss, the very group that OTC devices are designed for, may be particularly vulnerable to these distortions, since they have relatively good sensitivity to sounds and their detailed characteristics.
To investigate, 14 adults with normal hearing were fitted with four different OTC devices representing a range of styles currently on the market: Lexie B2 Plus (a traditional behind-the-ear style), Eargo (an invisible in-the-canal style) and Apple Air pods Pro 2 (representing the growing category of consumer earbuds that can function as hearing aids).
Each participant completed a set of spatial listening tasks while wearing each device, and also without any device as a baseline. The tasks were designed to probe three distinct aspects of spatial perception: (1) Azimuth identification tests whether a listener can accurately judge the horizontal direction of a sound source; (2) Front-back discrimination asks whether listeners can tell whether a sound is coming from in front of them or behind; (3) Sound externalization refers to whether sounds are perceived as coming from the outside world, or from inside the head like when listening over headphones.
The results were clear: every OTC device tested disrupted spatial perception (Figure 1). However, the specific aspects of spatial perception that were affected, and the extent of the disruption, depended on the device and on the individual. By examining these patterns, we are able to make inferences about which features of OTC hearing aids support spatial perception and which features have a disrupting effect.
Figure 1. Mean absolute externalization ratings across hearing aid conditions, with individual participant data overlaid.
As the market for consumer hearing devices continues to grow, it is important to understand how they affect all aspects of hearing, not just speech clarity. This will be essential for helping people make informed choices about hearing aids and for designing more natural-sounding hearing aids in the future.
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
Today we schedule a medical imaging scan in a clinic, doctor’s office, or at a hospital. What if we could self-monitor with a wearable unit?
We are working towards enabling practical wearable units and have taken two key steps to making this a reality.
First, in this work we show a method to shrink the electronics needed for an ultrasound machine to fit your body. It uses the strength of AI computer vision to “see” with a sensor that is stripped down in capability and missing bits of information; AI fills in the missing information. This allows us to implement a wider field of view, about 4 times wider what has been previously achieved.
Figure 1: Wearable ultrasound unit placed on the abdomen and images displayed on a smartphone (Image by Google Gemini 4/2026)
Second, it uses a new type of ultrasound sensor that is based on the same technology of high-volume commercial semiconductors, so it can be made cheaply, and with high accuracy. With less expensive sensors, we can implement larger arrays at a still reasonable cost. In our work we show a 2D pattern of elements which give us a 3D view into the body.
Putting together these two concepts we demonstrate a wearable imaging system a bit larger than a deck of playing cards. It has a wide field of view and can continuously scan the body in the course of daily activities using ultrasound. There are many potential applications of the technology demonstrated in this unit – abdominal imaging monitoring for bladder volume or fluid in the lungs, heart imaging for ejection fraction, large vessel imaging to sense oxygenation levels, and many more.
Fig. 1: Schematic of a typical scenario in which a sensor on the seabed measures interface waves in addition to acoustic waves along different paths. Right panel shows the OBX sensor package.
Fig. 1: Schematic of a typical scenario in which a sensor on the seabed measures interface waves in addition to acoustic waves along different paths. Right panel shows the OBX sensor package.
Fig.2: The trace of the particle motion (hodogram) in the source-to-receiver direction (radial, shown in pink), in the vertical direction (normal to the seabed, shown in yellow). The red curve shows the path of the particle in the vertical plane containing the source and receiver.
Fig.2: The trace of the particle motion (hodogram) in the source-to-receiver direction (radial, shown in pink), in the vertical direction (normal to the seabed, shown in yellow). The red curve shows the path of the particle in the vertical plane containing the source and receiver.
Fig.3: Ratio of the horizontal to vertical (HVSR) particle motion amplitude as a function of frequency (right panel). Particle motion was simulated for an ocean environment as shown in the left panel.
Fig.3: Ratio of the horizontal to vertical (HVSR) particle motion amplitude as a function of frequency (right panel). Particle motion was simulated for an ocean environment as shown in the left panel.
Figure 1 – Soundprint User Interface. Customers measure fifteen seconds of noise and then rate their ability to have a conversation (courtesy of SoundPrint).
Figure 2 – A charming restaurant that serves delicious food; however, guests may have a hard time hearing each other during peak hours.
Figure 3: Noise levels found at home and in restaurants. Adapted from “Present Methods of Sound Measurement” by A.H. Davis, (Architects’ Journal, May 1938)
Figure 1. Laboratory tank (side view).
Figure 2. Camera footage of the laboratory tank for labeling.
Figure 3. Roadmap of how each audio recording was processed and classified.
Figure 1. Perception when two different vowels are played to the two ears at different pitch. 
Figure 2. Perception when two different vowels are played to the two ears at the same pitch. 
Figure 1. Mean absolute externalization ratings across hearing aid conditions, with individual participant data overlaid.
Figure 1: Wearable ultrasound unit placed on the abdomen and images displayed on a smartphone (Image by Google Gemini 4/2026)