Fire Hydrant Hydrophones Find Water Leaks #ASA184

Fire Hydrant Hydrophones Find Water Leaks #ASA184

Locating leaks in water distribution networks is made easier with hydrant-mounted hydrophones and advanced algorithms.

Media Contact:
Ashley Piccone
AIP Media
301-209-3090
media@aip.org

CHICAGO, May 11, 2023 – Access to clean drinking water is essential for healthy communities, but delivering that water is growing increasingly difficult for many utilities. Corroding pipes and land shifts in aging water distribution networks can create frequent leaks, wasting water before it ever gets to the tap. Utilities in the U.S. lose about 6 billion gallons of water a day — enough to fill 9,000 swimming pools — due to leaks, in addition to wasted energy and resources spent in collecting and treating that water.

An overview of the methodology utilized for leak identification consisting of collecting acoustic data, extracting relevant features, and employing advanced machine learning and probabilistic models for leak detection and localization. Credit: Pranav Agrawal

Pranav Agrawal and Sriram Narasimhan from the University of California, Los Angeles will discuss an innovative acoustic solution to identify and track leaks in water distribution networks in their talk, “Maximum likelihood estimation for leak localization in water distribution networks using in-pipe acoustic sensing.” The presentation will take place Thursday, May 11, at 12:25 p.m. Eastern U.S. in the Purdue/Wisconsin room, as part of the 184th Meeting of the Acoustical Society of America running May 8-12 at the Chicago Marriott Downtown Magnificent Mile Hotel.

Detecting a leak in a single straight pipe is not a challenge, but large urban networks can be a grid of hundreds or thousands of pipes, and precisely locating a leak is no easy task. Acoustic monitoring is the go-to solution, as the sounds from leaks are unique and travel far in water, but even this method struggles in complex pipe networks.

“Localization of the leak is complex as it involves factors like hydrophone density, the frequency bandwidth of the leak sound, and material properties of the pipe,” said Agrawal. “It is impractical to have highly dense sensing that can localize leaks at any location in the network.”

To tackle the problem, the researchers developed algorithms that operate on acoustic signals collected via hydrophones mounted on the most accessible parts of the pipe network: fire hydrants.

“We have developed algorithms which operate on acoustic data collected from state-of-the-art monitoring devices mounted on fire hydrants and ‘listen’ to the sound produced by leaks inside the water column,” said Agrawal. “This device is now commercially available through Digital Water Solutions and has been deployed in various locations in Canada and the U.S., including in ongoing demonstration trials at the Naval Facilities Engineering and Expeditionary Warfare Center, Ventura County in California.”

Attaching their sensors to fire hydrants means the team can avoid costly excavation and reposition the devices as needed. Combined with novel probabilistic and machine-learning techniques to analyze the signals and pinpoint leaks, this technology could support water conservation efforts, especially in the Western U.S, where this is direly needed.

Reference in this release to the U.S. Navy Facilities Engineering and Expeditionary Warfare Center (NAVFAC-EXWC) does not imply endorsement of an individual contractor or solution by NAVFAC-EXWC, the U.S. Navy, or the U.S. Department of Defense.

———————– MORE MEETING INFORMATION ———————–
Main meeting website: https://acousticalsociety.org/asa-meetings/
Technical program: https://eppro02.ativ.me/web/planner.php?id=ASASPRING23&proof=true

ASA PRESS ROOM
In the coming weeks, ASA’s Press Room will be updated with newsworthy stories and the press conference schedule at https://acoustics.org/asa-press-room/.

LAY LANGUAGE PAPERS
ASA will also share dozens of lay language papers about topics covered at the conference. Lay language papers are 300 to 500 word summaries of presentations written by scientists for a general audience. They will be accompanied by photos, audio, and video. Learn more at https://acoustics.org/lay-language-papers/.

PRESS REGISTRATION
ASA will grant free registration to credentialed and professional freelance journalists. If you are a reporter and would like to attend the meeting or virtual press conferences, contact AIP Media Services at media@aip.org.  For urgent requests, AIP staff can also help with setting up interviews and obtaining images, sound clips, or background information.

ABOUT THE ACOUSTICAL SOCIETY OF AMERICA
The Acoustical Society of America (ASA) is the premier international scientific society in acoustics devoted to the science and technology of sound. Its 7,000 members worldwide represent a broad spectrum of the study of acoustics. ASA publications include The Journal of the Acoustical Society of America (the world’s leading journal on acoustics), JASA Express Letters, Proceedings of Meetings on Acoustics, Acoustics Today magazine, books, and standards on acoustics. The society also holds two major scientific meetings each year. See https://acousticalsociety.org/.

A virtual reality system to ‘test drive’ hearing aids in real-world settings

Matthew Neal – mathew.neal.2@louisville.edu
Instagram: @matthewneal32

Department of Otolaryngology and other Communicative Disorders
University of Louisville
Louisville, Kentucky 40208
United States

Popular version of 3pID2 – A hearing aid “test drive”: Using virtual acoustics to accurately demonstrate hearing aid performance in realistic environments
Presented at the 184 ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0018736

Many of the struggles experienced by patients and audiologists during the hearing aid fitting process stem from a simple difficulty: it is really hard to describe in words how something will sound, especially if you have never heard it before. Currently, audiologists use brochures and their own words to counsel a patient during the hearing aid purchase process, but a device often must be purchased first before patients can try them in their everyday life. This research project has developed virtual reality (VR) hearing aid demonstration software which allows patients to listen to what hearing aids will sound like in real-world settings, such as noisy restaurants, churches, and the places where they need devices the most. Using the system, patient can make more informed purchasing decisions and audiologists can program hearing aids to an individual’s needs and preferences more quickly.

This technology can also be thought of as a VR ‘test drive’ of wearing hearing aids, letting audiologists act as tour guides as patients try out features on a hearing aid. After turning a new hearing aid feature on, a patient will hear the devices update in a split second, and the audiologist can ask, “Was it better before or after the adjustment?” On top of getting device settings correct, hearing aid purchasers must also decide which ‘technology level’ they would like to purchase. Patients are given an option between three to four technology levels, ranging from basic to premium, with an added cost of around $1,000 per increase in level. Higher technology levels incorporate the latest processing algorithms, but patients must decide if they are worth the price, often without the ability to hear the difference. The VR hearing aid demonstration lets patients try out these different levels of technology, hear the benefits of premium devices, and decide if the increase in speech intelligibility or listening comfort is worth the added cost.

A patient using the demo first puts on a custom pair of wired hearing aids. These hearing aids are the same devices sold that are sold in audiology clinics, but their microphones have been removed and replaced with wires for inputs. The wires are connected back to the VR program running on a computer which simulates the audio in a given scene. For example, in the VR restaurant scene shown in Video 1, the software maps audio in a complex, noisy restaurant to the hearing aid microphones while worn by a patient. The wires send the audio that would have been picked up in the simulated restaurant to the custom hearing aids, and they process and amplify the sound just as they would in that setting. All of the audio is updated in real-time so that a listener can rotate their head, just as they might do in the real world. Currently, the system is being further developed, and it is planned to be implemented in audiology clinics as an advanced hearing aid fitting and patient counseling tool.

Video 1: The VR software being used to demonstrate the Speech in Loud Noise program on a Phonak Audeo Paradise hearing aid. The audio in this video is the directly recorded output of the hearing aid, overlaid with a video of the VR system in operation. When the hearing aid is switched to the Speech in Loud noise program on the phone app, it becomes much easier and more comfortable to listen to the frontal talker, highlighting the benefits of this feature in a premium hearing aid.

Text-to-Audio Models Make Music from Scratch #ASA183

Text-to-Audio Models Make Music from Scratch #ASA183

Much like machine learning can create images from text, it can also generate sounds.

Media Contact:
Ashley Piccone
AIP Media
301-209-3090
media@aip.org

NASHVILLE, Tenn., Dec. 7, 2022 – Type a few words into a text-to-image model, and you’ll end up with a weirdly accurate, completely unique picture. While this tool is fun to play with, it also opens up avenues of creative application and exploration and provides workflow-enhancing tools for visual artists and animators. For musicians, sound designers, and other audio professionals, a text-to-audio model would do the same.

The algorithm transforms a text prompt into audio. Credit: Zach Evans

As part of the 183rd Meeting of the Acoustical Society of America, Zach Evans, of Stability AI, will present progress toward this end in his talk, “Musical audio samples generated from joint text embeddings.” The presentation will take place on Dec. 7 at 10:45 a.m. Eastern U.S. in the Rail Yard room, as part of the meeting running Dec. 5-9 at the Grand Hyatt Nashville Hotel.

“Text-to-image models use deep neural networks to generate original, novel images based on learned semantic correlations with text captions,” said Evans. “When trained on a large and varied dataset of captioned images, they can be used to create almost any image that can be described, as well as modify images supplied by the user.”

A text-to-audio model would be able to do the same, but with music as the end result. Among other applications, it could be used to create sound effects for video games or samples for music production.

But training these deep learning models is more difficult than their image counterparts.

“One of the main difficulties with training a text-to-audio model is finding a large enough dataset of text-aligned audio to train on,” said Evans. “Outside of speech data, research datasets available for text-aligned audio tend to be much smaller than those available for text-aligned images.”

Evans and his team, including Belmont University’s Dr. Scott Hawley, have shown early success in generating coherent and relevant music and sound from text. They employed data compression methods to generate the audio with reduced training time and improved output quality.

The researchers plan to expand to larger datasets and release their model as an open-source option for other researchers, developers, and audio professionals to use and improve.

———————– MORE MEETING INFORMATION ———————–
Main meeting website: https://acousticalsociety.org/asa-meetings/
Technical program: https://eppro02.ativ.me/web/planner.php?id=ASAFALL22&proof=true

ASA PRESS ROOM
In the coming weeks, ASA’s Press Room will be updated with newsworthy stories and the press conference schedule at https://acoustics.org/asa-press-room/.

LAY LANGUAGE PAPERS
ASA will also share dozens of lay language papers about topics covered at the conference. Lay language papers are 300 to 500 word summaries of presentations written by scientists for a general audience. They will be accompanied by photos, audio, and video. Learn more at https://acoustics.org/lay-language-papers/.

PRESS REGISTRATION
ASA will grant free registration to credentialed and professional freelance journalists. If you are a reporter and would like to attend the meeting or virtual press conferences, contact AIP Media Services at media@aip.org.  For urgent requests, AIP staff can also help with setting up interviews and obtaining images, sound clips, or background information.

ABOUT THE ACOUSTICAL SOCIETY OF AMERICA
The Acoustical Society of America (ASA) is the premier international scientific society in acoustics devoted to the science and technology of sound. Its 7,000 members worldwide represent a broad spectrum of the study of acoustics. ASA publications include The Journal of the Acoustical Society of America (the world’s leading journal on acoustics), JASA Express Letters, Proceedings of Meetings on Acoustics, Acoustics Today magazine, books, and standards on acoustics. The society also holds two major scientific meetings each year. See https://acousticalsociety.org/.

Assessment of road surfaces using sound analysis

Andrzej Czyzewski – andcz@multimed.org

Multimedia Systems, The Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Pomorskie, 80-233, Poland

Jozef Kotus – Multimedia Systems, The Faculty of Electronics, Telecommunications and Informatics,
Grzegorz Szwoch – Multimedia Systems, The Faculty of Electronics, Telecommunications and Informatics],
Bozena Kostek – Audio Acoustics Lab., Gdansk Univ. of Technology, Gdansk, Poland

Popular version of 3pPAb1-Assessment of road surface state with acoustic vector sensor, presented at the 183rd ASA Meeting.

Have you ever listened to the sound of road vehicles passing by? Perhaps you’ve noticed that the sound differs depending on whether the road surface is dry or wet (for example, after the rain). This observation is the basis of the presented algorithm that assesses the road surface state using sound analysis.

Listen to the sound of a car moving on a dry road.
And this is the sound of a car on a wet road.

A wet road surface not only sounds different, but it also affects road safety for drivers and pedestrians. Knowing the state of the road (dry/wet), it is possible to notify the drivers about dangerous road conditions, for example, using signs displayed on the road.

There are various methods of assessing the road surface. For example, there are optical (laser) sensors, but they are expensive. Therefore, we have decided to develop an acoustic sensor that ‘listens” to the sound of vehicles moving along the road and determines whether the surface is dry or wet.

The task may seem simple, but we must remember that the sensor records the sound of road vehicles and other environmental sounds (people speaking, aircraft, animals, etc.). Therefore, instead of a single microphone, we use a special acoustic sensor built from six miniature digital microphones mounted on a small cube (10 mm side length). With this sensor, we can select sounds incoming from the road, ignoring sounds from other directions, and also detect the direction in which a vehicle moves.

Since the sound of road vehicles moving on a dry and wet surface differ, performing frequency analysis of the vehicle sounds is recommended.

The figures below present how the sound spectrum changes in time when a vehicle moves on a dry surface (left figure) and a wet surface (right figure). It is evident that in the case of a damp surface, the spectrum is expanded towards higher frequencies (the upper part of the plot) compared with the dry surface plot. Colors on the plot represent the direction of arrival of sound generated by vehicle passing by (the angle in degrees). You can observe how the vehicles moved in relation to the sensor.

Plots of the sound spectrum for cars moving on a dry road (left) and a wet road (right). Color denotes the sound source azimuth. In both cases, two vehicles moving in opposite directions were observed.Plots of the sound spectrum for cars moving on a dry road (left) and a wet road (right). Color denotes the sound source azimuth. In both cases, two vehicles moving in opposite directions were observed.

In our algorithm, we have developed a parameter that describes the amount of water on the road. The parameter value is low for a dry surface. However, as the road surface becomes increasingly wet during rainfall, the parameter value becomes more extensive.

The results obtained from our algorithm were verified by comparing them with data from a professional road surface sensor that measures the thickness of a water layer on the road using a laser beam (VAISALA Remote Road Surface State Sensor DSC111). The plot below shows the results from analyzing sounds recorded from 1200 road vehicles passing by the sensor, compared with data obtained from the reference sensor. The data were obtained from a continuous 6-hour observation period, starting from a dry surface, then observing rainfall until the road surface had dried.

A surface state measure calculated with the proposed algorithm and obtained from the reference device A surface state measure calculated with the proposed algorithm and obtained from the reference device

As one can see, the results obtained from our algorithm are consistent with data from the professional device. Therefore, the results are promising, and the cheap sensor is easy to install at multiple points within a road network. Hence, it makes the proposed solution an attractive method of road condition assessment for intelligent road management systems.

Presence of a drone and estimating its range simply from the drone audio emissions

Kaliappan Gopalan – kgopala@pnw.edu

Purdue University Northwest, Hammond, IN, 46323, United States

Brett Y. Smolenski, North Point Defense, Rome, NY, USA
Darren Haddad, Information Exploitation Branch, Air Force Research Laboratory, Rome, NY, USA

Popular version of 1ASP8-Detection and Classification of Drones using Fourier-Bessel Series Representation of Acoustic Emissions, presented at the 183rd ASA Meeting.

With the proliferation of drones – from medical supply and hobbyist to surveillance, fire detection and illegal drug delivery, to name a few – of various sizes and capabilities flying day or night, it is imperative to detect their presence and estimate their range for security, safety and privacy reasons.

Our paper describes a technique for detecting the presence of a drone, as opposed to environmental noise such as from birds and moving vehicles, simply from the audio emissions of the drone from its motors, propellers and mechanical vibrations. By applying a feature extraction technique that separates a drone’s distinct audio spectrum from that of atmospheric noise, and employing machine learning algorithms, we were able to identify drones from three different classes flying outdoors with correct class in over 78 % of cases. Additionally, we estimated the range of a drone from the observation point correctly to within ±50 cm in over 85 % of cases.

We evaluated unique features characterizing each type of drone using a mathematical technique known as the Fourier-Bessel series expansion. Using these features which not only differentiated the drone class but also differentiated the drone range, we applied machine learning algorithms to train a deep learning network with ground truth values of drone type, or its range as a discrete variable at intervals of 50 cm. When the trained learning network was tested with new, unused features, we obtained the correct type of drone – with a nonzero range – and a range class that was within the appropriate class, that is, within ±50 cm of the actual range.

Any point along the main diagonal line indicates correct range class, that is, within ±50 cm of actual range, while off-diagonal values correspond to false classification error.

For identifying more than three types of drones, we tested seven different types of drones, namely, DJI S1000, DJI M600, Phantom 4 Pro, Phantom 4 QP with a quieter set of propellers, Mavic Pro Platinum, Mavic 2 Pro, and Mavic Pro, all tethered in an anechoic chamber in an Air Force laboratory and controlled by an operator to go through a series of propeller maneuvers (idle, left roll, right roll, pitch forward, pitch backward, left yaw, right yaw, half throttle, and full throttle) to fully capture the array of sounds the craft emit. Our trained deep learning network correctly identified the drone type in 84 % of our test cases.  Figure 1 shows the results of range classification for each outdoor drone flying between a line-of-sight range of 0 (no-drone) to 935 m.

3pSP4 – Imaging Watermelons

Dr. David Joseph Zartman
Zartman Inc., L.L.C.,
zartman.david@gmail.com
Loveland, Colorado

Popular version of 3pSP4 – Imaging watermelons
Presented Wednesday afternoon, May 25, 2022
182nd ASA Meeting, Denver
Click here to read the abstract

When imaging watermelons, everything can be simplified down to measuring a variable called ripeness, which is a measure of the internal medium of the watermelon, rather than looking for internal reflections from any contents such as seeds. The optimal experimental approach acoustically is thus a through measurement, exciting the wave on one side and measuring the result on the other.

Before investigating the acoustic properties, it is useful to examine watermelons’ ripening properties from a material perspective.  As the fruit develops, it starts off very hard and fibrous with a thick skin. Striking an object like this would be similar to hitting a rock, or possibly a stick given the fibrous nature of the internal contents of the watermelon.

As the watermelon ripens, this solid fiber starts to contain more and more liquid, which also sweetens over time. This process continues and transforms the fruit from something too fibrous and bitter to something juicy and sweet. Most people have their own preference for exactly how crunchy versus sweet they personally prefer. The skin also thins throughout this process. As the fibers continue to be broken down beyond optimal ripeness, the fruit becomes mostly fluid, possibly overly sweet, and with a very thin skin.  Striking the fruit at this stage would be similar to hitting some sort of water balloon. While the sweet juice sounds like a positive, the overall texture at the stage is usually not considered desirable.

In review, as watermelons ripen, they transform from something extremely solid to something more resembling a liquid filled water balloon. These are the under-ripe and over-ripe conditions; thus, the personal ideal exists somewhere between the two. Some choose to focus on the crunchy earlier stage at the cost of some of the sweetness, possibly also preferable to anyone struggling with blood sugar issues, in contrast to those preferring to maximize the sweet juicy nature of the later stages at the cost of crunchy texture.

The common form of acoustic measurement in this situation is to simply strike the surface of the watermelon with a finger knuckle and listen to the sound. More accuracy is possible by feeling with fingertips on the opposite side of the watermelon when it is struck. Both young and old fruit do not have much response, one being too hard, getting an immediate sharp response and being more painful on the impacting finger. The other is more liquid and thus is more difficult to sonically excite. A young watermelon may make a sound described as a hard ‘tink’, while an old one could be described more as a soft ‘phlub’. In between, it is possible to feel the fibers in the liquid vibrating for a period of time, creating a sound more like a ‘toong’. A shorter resonance, ‘tong’, indicates younger fruit, while more difficulty getting a sound through, ‘tung’, indicates older.

An optimal watermelon can thus be chosen by feeling or hearing the resonant properties of the fruit when it is struck and choosing to preference.