–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
In this project, we created a lesson plan using Minecraft Education Edition to teach students about reverberation time (RT60) and material absorption. The goal was to make acoustic concepts more interactive and engaging by using a familiar game environment.
Screenshot of the Minecraft Education “Castle Tower” tutorial level or overview image of the project environment.
The experience begins with a “Castle Tower” tutorial level that introduces students to the variables used in the Sabine equation, which is commonly used to estimate reverberation time. After completing the tutorial, students enter a creative sandbox where they can build their own rooms and test how sound behaves inside them.
Full walkthrough video demonstrating the tutorial level, sandbox environment, room construction process, room scanning process, and RT60/audio simulation system.
Using custom MakeCode JavaScript, the system scans the room around the player to estimate its size and the materials used in its construction. The program then calculates an estimated RT60 value and generates a simulated sound decay that matches the room’s acoustic properties.
Different Minecraft building materials were assigned simplified absorption values. For example, carpet absorbs more sound than concrete. Students can experiment by changing room materials and immediately hearing how those changes affect the sound of the space.
larger room
smaller room
To use the system, students build a rectangular room, stand in the center, and run the “roomtest” command. The game then calculates the RT60 and plays a sound with a matching decay tail, allowing students to hear how the room responds acoustically.
This project demonstrates how Minecraft Education Edition can be used as a creative tool for teaching acoustic physics. By combining interactive building mechanics with real-time calculations and audio feedback, students are able to visualize, calculate, and hear how room acoustics work in a hands-on way.
–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–
More often than not, what crosses your mind when hearing “Liberty Bell” is not the chime or musicality of the bell; rather, it is likely the infamous crack through its left-hand side. On this 250th anniversary, it is worthwhile to pause in its silence, and consider how it might have sounded when newly cast in 1751.
Predicting the sound of a bell feels, at first pass, relatively straightforward. The bell has a simple geometry, which is important for understanding how its vibration patterns radiate sound to your ears. The bell is made of bronze, which is essential for predicting the exact pitches at which the bell will sound, as well as how long it rings. These parameters, considered together, define this action of vibrations along the surface pushing air into oscillation, leading to the radiation of sound and the bell’s characteristic chime. The physics are well understood, but the Liberty Bell, in particular, is not. That is, surprisingly little exists in the way of geometry and material composition for the bell, which makes it a particularly challenging example.
Figure 1. Photo of the Liberty Bell replica at Penn State Behrend
Simply using the meager information available online produces, frankly, a terrible sounding bell that in presence does not match the silhouette of the iconic bell. Rather than toy with parameters until things seemed “right,” we sought out a replica to measure its geometry and vibroacoustic response, so to calibrate our prediction. Modal Analysis, the act of exciting a structure to understand its vibration patterns – called modes – as well as its frequencies at which the modes vibrate, is a common tool used to isolate this information in a meaning and practical way. Some results from this modal analysis are compared in Figure 2, showing the measured vibration patterns for the replica to classical results from Rossing and Perrin [1].
Figure 2. Comparison of the first few vibration patterns between the replica and theory
Knowing the modes and frequencies was only half the effort, though, as we noted that the geometry defines so much of the sound that we eventually perceive. Indeed, small changes to the geometry could alter the prediction considerably. To have the model be as close to truth as possible, a 3D scan of the replica was done to produce a geometry – making it likely the most accurately modeled cast bell to ever exist! Once the geometry, frequencies, and modes are in place, the prediction could be tuned so to back out the bell’s material properties – “Bell Bronze”, intrinsic to the distinct ring of bells.
Figure 3. 3-dimensional scan of the replica to define the cross-section and model geometry
Through modal analysis of this replica, we were able to tune a predictive model of the bell to match the measured vibroacoustic response. Beyond the pretty shapes, the analysis tells us how pitches in the chime relate in strength and in time, illuminating the evolution of the sound over time and adding scientific context to something so often overlooked in the story of the Liberty Bell.
References:
[1] Rossing, Thomas D., and Robert Perrin. “Vibrations of bells.” Applied Acoustics 20.1 (1987): 41-70
Prashanth Tamilselvam – ptamilselvam@hawk.illinoistech.edu
Bluesky: @prashanth-t.bsky.social
Instagram: @prashanth_tamilselvam
Illinois Institute of Technology, Chicago, Illinois, 60616, United States
Francisco Ruiz ruiz@illinoistech.edu
Illinois Institute of Technology,
Chicago, Illinois,60616
United States
Popular version of 4pMU15 – Experiments on the flow acoustics of Human whistling
Presented at the 189th ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0041252
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
When was the last time you tried to whistle and wondered how do we make music with our mouth? For many, whistling feels effortless: purse your lips, blow, and a clear tone appears. Yet nearly half of us find it surprisingly difficult and never manage to produce more than a faint breath. Our research explores the physics behind this familiar but surprisingly complex activity.
When you whistle, the tongue rises against the roof of the mouth, leaving a small gap. The lips form a second constriction, and the space between acts as a resonant chamber, much like the tube of a flute. Pitch is controlled by moving the tongue to change the space between it and the palate. But geometry alone is not enough: we have found that only a specific combination of airflow and lip shape creates a ‘sweet spot’ leading to a stable tone. Maybe this is why so many people struggle with it.
Figure 1
In our experiments, involving orifices shaped like the hole of a donut to represent the lips, we found periodic vortices coming out (fig 1). These vortices are released at a frequency that is exactly the pitch we hear, showing that whistling is not simply blowing air but a precise coupling between the flow and the sound (fig 2a). The shape of the lips has a significant influence on the sound. Too narrow or too wide an opening suppresses the sound, and the front-to-back contour of the lips must encourage clean airflow separation (see how the non-toroidal lip geometry in fig 2b manages to whistle only within a small range of air velocity). This subtle control of lip geometry is essential for sustaining a clear, steady whistle.
Figure 2
The sound does not simply travel outward into the air. It also travels back into the mouth, where it interacts with the air coming from the lungs. This inward-traveling sound creates a feedback loop that amplifies the oscillations of the flow (fig 2c). The shear layer produced at the back of the mouth has a strong influence on how the airflow interacts with the lips. Subtle changes in this upstream shear layer either support or disrupt the formation of the vortices, and hence the sound.Difficult? It clearly is for many of us, but did you know that walruses also whistle? And they shape their lips exactly the way humans do it.We hope that understanding how humans (and walruses) whistle will help those of us who struggle with it. Meanwhile, our research is already guiding the development of a new, super-compact wind instrument that can be played without the use of hands. We call it the Flutino.Whistling may feel ordinary, but its physics is anything but simple.
Monterey Bay Aquarium Research Institute, Moss Landing, CA, 95039, United States
Popular version of 4aUW7 – Wind-driven movement ecology of blue whales detected by acoustic vector sensing
Presented at the 188th ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0038108
–The research described in this Acoustics Lay Language Paper may not have yet been peer reviewed–
A technology that captures multiple dimensions of underwater sound is revealing how blue whales live, thereby informing whale conservation.
The most massive animal ever to evolve on Earth, the blue whale, needs a lot of food. Finding that food in a vast foraging habitat is challenging, and these giants must travel far and wide in search of it. The searching that leads them to life-sustaining nutrition can also lead them to a life-ending collision with a massive fast-moving ship. To support the recovery of this endangered species, we must understand where and how the whales live, and how human activities intersect with whale lives.
Toward better understanding and protecting blue whales in the California Current ecosystem, an interdisciplinary team of scientists is applying a technology called an acoustic vector sensor. Sitting just above the seafloor, this technology receives the powerful sounds produced by blue whales and quantifies changes in both pressure and particle motion that are caused by the sound waves. The pressure signal reveals the type of sound produced. The particle motion signal points to where the sound originated, thereby providing spatial information on the whales.
A blue whale in the California Current ecosystem. Image Credit: Goldbogen Lab of Stanford University / Duke Marine Robotics and Remote Sensing Lab; NMFS Permit 16111.
For blue whales, it is all about the thrill of the krill. Krill are small-bodied crustaceans that can form massive swarms. Blue whales only eat krill, and they locate swarms to consume krill by the millions (would that be krillions?). Krill form dense swarms in association with cold plumes of water that result from a wind-driven circulation called upwelling. Sensors riding on the backs of blue whales reveal that the whales can track cold plumes precisely and persistently when they are foraging.
The close relationships between upwelling and blue whale movements motivates the hypothesis that the whales move farther offshore when upwelling habitat expands farther offshore, as occurs during years with stronger wind-driven upwelling. We tested this hypothesis by tracking upwelling conditions and blue whale locations over a three-year period. As upwelling doubled over the study period, the percentage of blue whale calls originating from offshore habitat also nearly doubled. A shift in habitat occupancy offshore, where the shipping lanes exist, also brings higher risk of fatal collisions with ships.
However, there is good news for blue whales and other whale species in this region. Reducing ship speeds can greatly reduce the risk of ship-whale collisions. An innovative partnership, Protecting Blue Whales and Blue Skies, has been fostering voluntary speed reductions for large vessels over the last decade. This program has expanded to cover a great stretch of the California coast, and the growing participation of shipping companies is a powerful and welcome contribution to whale conservation.
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. Photo of the Liberty Bell replica at Penn State Behrend
Figure 2. Comparison of the first few vibration patterns between the replica and theory
Figure 3. 3-dimensional scan of the replica to define the cross-section and model geometry
When was the last time you tried to whistle and wondered how do we make music with our mouth? For many, whistling feels effortless: purse your lips, blow, and a clear tone appears. Yet nearly half of us find it surprisingly difficult and never manage to produce more than a faint breath. Our research explores the physics behind this familiar but surprisingly complex activity.
Figure 1
A blue whale in the California Current ecosystem. Image Credit: Goldbogen Lab of Stanford University / Duke Marine Robotics and Remote Sensing Lab; NMFS Permit 16111.