Listen to the Toilet — It Could Detect Disease #ASA183

Listen to the Toilet — It Could Detect Disease #ASA183

Microphone sensor and machine learning can classify excretion events, identify cholera or other bowel diseases, all without identifiable information.

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

NASHVILLE, Tenn., Dec. 5, 2022 – Cholera, a bacterial disease that induces diarrhea, affects millions of people and results in about 150,000 deaths each year. Identifying potential communal disease spread for such an outbreak would alert health professionals early and improve the allocation of resources and aid. However, for obvious reasons, monitoring this and other bowel diseases is a sensitive matter.

The sensor in use over a toilet. Credit: Maia Gatlin

In her presentation, “The feces thesis: Using machine learning to detect diarrhea,” Maia Gatlin of the Georgia Institute of Technology will describe how a noninvasive microphone sensor could identify bowel diseases without collecting any identifiable information. The presentation will take place Dec. 5 at 4:35 p.m. Eastern U.S. in Summit C, as part of the 183rd Meeting of the Acoustical Society of America running Dec. 5-9 at the Grand Hyatt Nashville Hotel.

Gatlin and her team tested the technique on audio data from online sources. Each audio sample of an excretion event was transformed into a spectrogram, which essentially captures the sound in an image. Different events produce different features in the audio and the spectrogram. For example, urination creates a consistent tone, while defecation may have a singular tone. In contrast, diarrhea is more random.

Spectrogram images were fed to a machine learning algorithm that learned to classify each event based on its features. The algorithm’s performance was tested against data with and without background noises to make sure it was learning the right sound features, regardless of the sensor’s environment.

“The hope is that this sensor, which is small in footprint and noninvasive in approach, could be deployed to areas where cholera outbreaks are a persistent risk,” said Gatlin. “The sensor could also be used in disaster zones (where water contamination leads to spread of waterborne pathogens), or even in nursing/hospice care facilities to automatically monitor bowel movements of patients. Perhaps someday, our algorithm can be used with existing in-home smart devices to monitor one’s own bowel movements and health!”

In the future, Gatlin and her colleagues plan to gather real-world acoustic data so that their machine learning model can adapt to work in a variety of bathroom environments.

———————– 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/.

Machine Learning Diagnoses Pneumonia by Listening to Coughs #ASA183

Machine Learning Diagnoses Pneumonia by Listening to Coughs #ASA183

A new algorithm could spot early signs of respiratory diseases in hospitals and at home.

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

NASHVILLE, Tenn., Dec. 5, 2022 – Pneumonia is one of the world’s leading causes of death and affects over a million people a year in the United States. The disease disproportionately impacts children, older adults, and hospitalized patients. To give them the greatest chance at recovery, it is crucial to catch and treat it early. Existing diagnosis methods consist of a range of blood tests and chest scans, and a doctor needs to suspect pneumonia before ordering them.

A machine learning algorithm identifies cough sounds and determines whether the subject is suffering from pneumonia. Credit: Jin Yong Jeon

Jin Yong Jeon of Hanyang University will discuss a technique to diagnose pneumonia through passive listening in his session, “Pneumonia diagnosis algorithm based on room impulse responses using cough sounds.” The presentation will take place Dec. 5 at 4:20 p.m. Eastern U.S. in Summit C, as part of the 183rd Meeting of the Acoustical Society of America running Dec. 5-9 at the Grand Hyatt Nashville Hotel.

Jeon and fellow researchers developed a machine learning algorithm to identify cough sounds and determine whether the subject was suffering from pneumonia. Because every room and recording device is different, they augmented their recordings with room impulse responses, which measure how the acoustics of a space react to different sound frequencies. By combining this data with the recorded cough sounds, the algorithm can work in any environment.

“Automatically diagnosing a health condition through information on coughing sounds that occur continuously during daily life will facilitate non-face-to-face treatment,” said Jeon. “It will also be possible to reduce overall medical costs.”

Currently, one company has plans to apply this algorithm for remote patient monitoring. The team is also looking to implement it as an app for in-home care, and they plan to make the experience simpler and more user-friendly.

“Our research team is planning to automate each step-by-step process that is currently performed manually to improve convenience and applicability,” said Jeon.

———————– 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/.

2pCA8 – Sonic boom propagation using an improved ray tracing technique

Kimberly Riegel – kriegel@qcc.cuny.edu
William Costa
George Seaton
Christian Gomez
Queensborough Community College
222-05 56th Avenue
Bayside, NY 11364

Popular version of 2pCA8 – Sonic boom propagation in a non-homogeneous atmosphere using a stratified ray tracing technique’
Presented Tuesday afternoon, November 30, 2019
181st ASA Meeting
Click here to read the abstract

Supersonic air travel could reduce flight times by half, vastly improving long range air travel. To make this type of travel commercially viable, however, the current ban on overland flight would need to be lifted while ensuring residents below are still protected from the high noise levels in the flight paths of these new aircraft. There has been a recent increase in supersonic aircraft investment. United Airlines just invested in 15 supersonic jets provided by BOOM supersonic. These aircraft are expected to fly in 2029 but will remain restricted to over water flight. Lockheed Martin in partnership with NASA is building a low boom demonstrator aircraft. This aircraft is expected to perform some community-based test flights next year. Therefore, a computationally efficient prediction tool that can predict the impact of sonic booms in urban areas would be a useful tool for researchers and legislators.

Previously a ray tracing simulation tool to predict the sound behavior in urban environments was developed. The simulation included the ability to read in 3D renderings of the environments. This made it possible to simulate any complicated shape including detailed buildings and multiple buildings. All surfaces are represented by a mesh of triangular faces. The more complicated the building, the more triangles were required to accurately represent it. The biggest limitation of the code was that it could take several days to complete one simulation of a complicated building. The purpose of this work is to reduce the computational time to make the numerical simulation more accessible while not sacrificing the accuracy of the results.

In order to reduce the computation time for complex geometries the entire environment was cut into horizontal slices. Only the slice where the origin of the ray is considered at a time. This allows for a significant reduction in the number of building facets that needs to be assessed for each step. Figure 1 shows the total building in grey and the slice under consideration in green.

 

[IMAGE MISSING]
Figure 1. Representation of a simple building/ray interaction and the vertical slices where the building is segmented.

To determine how the modifications to the code improved the result, several environments were run and compared to those environments for previous version of the code. Table 1 shows the improvements. From the timing of the different versions of the code it is clear that updates to the code have drastically reduced the computation times for complex environments. The resulting pressures at the receivers have no noticeable difference in the pressure results. This will improve the useability of the simulation and make it more convenient to predict sonic booms in urban areas.

2aCA11-Validating a phase-inversion procedure to assess the signal-to-noise ratios at the output of hearing aids with wide-dynamic-range compression

Donghyeon Yun1 – dongyun@iu.edu
Yi Shen2 – shenyi@uw.edu
Jennifer J Lentz1 – jjlentz@indiana.edu

1. Department of Speech, Language and Hearing Sciences, Indiana University Bloomington,
2631 East Discovery Parkway Bloomington, IN 47408
2. Department of Speech and Hearing Sciences, University of Washington,
1417 Northeast 42nd Street, Seattle, WA 98105-6246

Popular version of 2aCA11 – Measuring hearing aid compression algorithm preference with the Tympan
Presented at the 181st ASA Meeting
Click here to read the abstract

Speech understanding is challenging in background noise, especially for listeners with hearing loss. Although the use of hearing aids may be able to compensate for the loss of hearing sensitivity by amplifying incoming sounds, the target speech and background noise are often amplified together. In this way, hearing aids do not “boost” the signal with respect to the noise. Although hearing aids will make the sounds louder, common processing in these devices may even make the signal smaller relative to the noise. This is because the techniques used to boost soft sounds but not loud ones are nonlinear in nature. The amount of the signal relative to the noise is called the Signal to Noise Ratio, or the SNR. A lower SNR at the output of a hearing aid may make speech understanding more difficult. Thus, it is important to accurately assess the output SNR when prescribing hearing aids in an audiology clinic.

——————–  The phase-inversion technique —————

In this paper, we looked to see whether a specific technique used to determine the SNR at the output of a hearing aid gave accurate results. In this phase-inversion technique, the hearing aid’s response to a target speech sound (S) embedded in background noise (N) is recorded. We also collect responses with an “inverted” signal (S’) and an “inverted” noise (N’). By using these inverted signals, we can calculate the SNR at the output of the hearing aid.
It has been difficult to determine whether this technique gives an accurate estimate of SNR because there is no way to calculate the true SNR at the output of a hearing aid. However, we can do this with a simulated hearing aid. In the current study, we calculated true output SNR using the hearing aid simulation for a number of test conditions. We then compared these true values to values estimated using the phase-inversion technique under the same test conditions. The test conditions included: (1) various SNRs at the input of the simulated hearing aid, (2) hearing-aid configurations fitted to four typical profiles of hearing loss, (3) two types of background noise (two- and twenty-talker babble noises), and (4) various parameters of the nonlinear processing algorithm.

——————- The output SNRs estimated using the phase-inversion technique (symbols) agree well with the actual output SNRs (symbols) ——————-

In agreement with previous studies, the output SNR for the simulated hearing aid was different from the input SNR, and this mismatch between the output and input SNRs depended on the test condition. The differences between the actual and estimated output SNRs were very small, indicating satisfactory validity for the phase-inversion technique.