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.

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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.

Version Timings
12 faces 192 faces 768 faces 3072 faces
Original Python 362 min 610 min 2055 min ~ 5 days
Stratified Building 280 min 229 min 669 min 558 min

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.