Valentin V. Gravirov –  vvg@ifz.ru
Russian Federation
Moscow 123242

Popular version of paper 1aSPa4
Presented Tuesday morning, June 8, 2021
180^th ASA Meeting, Acoustics in Focus

Good afternoon, dear readers! I represent a research team from Russia and in this brief sci-pop summary, I would like to tell you about the essence of the work carried out recently. Our main goal was to study the sound generated by drones during flight in order to solve the problems of their automatic finding and recognition. It’s no secret that unmanned aerial vehicles or drones are now developing and progressing extremely fast. The drones are beginning to be used everywhere, for example, for filming, searching for missing people, delivery of documents and small packages. Obviously, over time, the number of tasks completed and the number of unmanned aerial vehicles will continue to increase. This will inevitably lead to an increase in the number of collisions in the air >.

Last year, as part of our expedition to the Arctic region, we personally encountered a similar problem.

Our expeditionary team used two drones to photograph a polar bear, which nearly caused them to collide. That is, two quadrocopters almost collided in circumstances when there was no other drone within a radius of a thousand kilometers. Imagine the danger of air traffic, when many devices are flying nearby? Within the framework of civilian use, such a problem can be solved by using active radio beacons on drones, but in official use, for example, in military tasks, it is obvious that such systems will be unacceptable. To solve such problems, a large number of optical systems for recognizing drones have already been created, but they do not always give a accurate results and often significantly depend on weather conditions or the time of day. That is why our research group has set itself the goal of studying the acoustic noise generated by unmanned aerial vehicles, this will allow us to find new ways to solve the urgent problem of detecting and determining the location of drones.

In the course of the experiments, the sound generated by typical electric motors of drones with the installation of propellers with different numbers of blades were studied in detail. The analysis of the results obtained allowed us to conclude that the main factor to the noise is created by the rotational speed of the blades, which is equal to the rotational speed of the engine shaft, multiplied by the number of blades. At the same time, due to the presence of small defects in the blades, the sound of each specific blade are slightly different. The studies also examined the noise generated by two popular household drone models DJI Mavic

Used household drone models DJI Mavic.  in dense urban environments with high levels of urban acoustic noise. It was found that at distances exceeding 30 meters, the acoustic signal level disappears in the background to urban noise, which can be explained by the small size and small power of the models studied. Undoubtedly, outside the city or in a quiet place, the detection range of drones will be significantly greater. In the course of the experiments, it was found that the main sound generated by drones lie in the frequency range 100 – 2000 Hz

In addition to field experiments, mathematical modeling was also carried out, the results of which coincide with the obtained experimental data. An algorithm based on the use of artificial neural networks technology has been developed for automated recognition of drones. At the current time, the algorithm allows detecting a drone with a 94% accuracy. Unfortunately, the probability of false positives is still high and amounts to about 12%. This will require us to carry out in the near future both additional research and work on a significant improvement of the recognition algorithm.

Kendal Leftwich – kmleftwi@uno.edu
George Druant – George.Drouant@oit.edu
Julia Robe – jerobe@uno.edu
Juliette Ioup – jioup@uno.edu

University of New Orleans
2000 Lakeshore Drive
New Orleans, LA 70148

Determining the range to marine mammals in the Northern gulf of Mexico via bayesian acoustic signal processing
Presented Acoustic Localization IV afternoon, December 8, 2020
179^th ASA Meeting, Acoustics Virtually Everywhere

The Littoral Acoustic Demonstration Center – Gulf Ecological Monitoring and Modeling (LADC-GEMM) has been collecting underwater acoustic data in the Northern Gulf of Mexico (GoM) since 2002 through 2017.  Figure 1 shows the collection sites and the location of the BP oil spill in April 2010.  The data are collected by a hydrophone, an underwater microphone, which records the acoustic signals or sounds of the region.

One of the goals of the research at the University of New Orleans (UNO) is to identify individual marine mammals by their acoustic signal.  Part of this identification includes being able to locate them.   In this paper we will briefly explain how we are attempting to locate sperm whales in the GoM.

First, we need to understand how the whale’s sounds travel through the water and what happens to them as they do.  Any sound that travels through a medium (air, water, or any material) will have its loudness decreased.  For example, it is much easier to hear a person talking to you when you are in the same room, but if they are talking to you through a wall their voice level is reduced because the signal travels through a medium (the wall) that reduces its loudness.  Therefore, as the whale signal travels through the GoM to our hydrophones the loudness of the signal is reduced.  The impact that this has on the whale’s signal is determined by the temperature, the depth of the recording device below the surface, the salinity, and the pH level of the water.  Using this information, we can determine how much the loudness of the whale’s signal will decrease per kilometer that the signal travels.  This can be seen in figure 2.

We will use the known signal loudness of the sound emitted by a sperm whale and the recorded loudness of the signal along with the impact of the GoM on the signal to determine how far away the sperm whale is from our hydrophone.   Unfortunately, due to technical limitations of the equipment we can only do this for a single hydrophone so we cannot currently locate the sperm whale’s exact position. We can only tell you where it is located at a certain distance around the hydrophone.  Figures 3 shows graphically the results of our calculations for two of the 276 sperm whale signals we used with our model to estimate how far away the whale is from our hydrophone.

Keegan Yi Hang Sim – yhsim@connect.ust.hk
Yijia Chen
Yuxuan Wan
Kevin Chau

Department of Electronic and Computer Engineering
Hong Kong University of Science and Technology
Clear Water Bay
Hong Kong

Popular version of paper 2aSP2 Advanced automobile crash detection by acoustic methods
Presented Tuesday morning, December 03, 2019
178th Meeting, San Diego, CA
Read the article in Proceedings of Meetings on Acoustics

Introduction
Self-driving cars are currently a major interest for engineers around the globe. They incorporate more advanced versions of steering and acceleration control found in many of today’s cars. Cameras, radars, and lidars (light detection and ranging) are frequently used to detect obstacles and automatically brake to avoid collision. Air bags, which have been in use as early as 1951, soften the impact during an actual collision.

Vision Zero, an ongoing multinational effort, hopes that all car crashes will eventually be eliminated, and self-driving autonomous vehicles are likely to play a key role in achieving this. However, current technology is unlikely to be enough, as it does not works poorly in low light conditions. We believe that using sound, although it provides less which carries a unique information, is also important as it can be used in all scenarios and also likely performs much better.

Sound waves travel as fast as seventeen times faster in a car than at 1/3 of a kilometer per second in the air, which leads to much faster detection by using sound instead of acceleration, and clearly is not affected by light, air quality, and other factors. Previous research was able to use sound to detect collisions and sirens, but by the time a collision occurs, it is far too late. So instead we want to identify sounds that frequently occur before car crashes, such as tire skidding, honking, and sometimes screaming to figure out the direction they are coming from. Thus, we have designed a method to predict a car crash by detecting and isolating the sounds of tire skidding that might signal a possible crash.

Algorithm
The algorithm utilizes the discrete wavelet transform (DWT), which decomposes a sound wave into high- and low-frequency components in time all sorts of tiny waves each lasting for a short period in time. This can be done repeatedly, yielding a series of components of various frequencies. Using wavelets is significantly faster and gives much more accurate and precise results representation of transient events associated with car crashes than elementary techniques such as the Fourier Transform, which transforms a sound into its frequency steady oscillation components. Previous methods to detect car crashes examined the highest frequency components, but tire skidding only contains lower frequency components, whereas a collision contains almost all frequencies.

One can hear in the original audio of a car crash the three distinct sections: honking, tire skidding, and the collision.

The top diagram shows the audio displayed as a waveform, plotted against time. The bottom shows a spectrogram of the audio, with frequency on the y-vertical axis and time on the horizontal x-axis, and the brightness of the color representing the magnitude of a particular frequency component. This was created using a variation of the Fourier Transform. One can observe the differences in appearance between honking, tire skidding, and collision, which suggests that mathematical methods should be able to detect and isolate these. We can also see that the collision occupies all frequencies while tire skidding occupies lower frequencies with two distinct sharp bands at around 2000Hz.

“OutCollision.wav , the isolated audio containing just that isolates the car crash”

Using our algorithm, we were able to create audio files containing just that isolate the honking, tire skidding, as well as the collision. One can hear that they doThey may not sound like normal honking, tire skidding or collisions, which is a byproduct of our algorithm. Fortunately, but this does not affect the ability to detect the tire skidding various events by a computer.

Conclusion
The algorithm performs well for detecting the honking and tire skidding, and is fast enough to be done in real time, before acceleration information can be processed which would be great for the raising the alert of a possible crash, and for activating the hazard lights and seatbelt pre-tensioners. The use of sound in cars is a big step forward for the analysis prevention of car crashes, as well as improving autonomous and driverless vehicles and achieving Vision Zero, by providing a car with more timely and valuable information about its surroundings.

Eoin A King – eoking@hartford.edu
Akin Tatoglu
Digno Iglesias
Anthony Matriss
Ethan Wagner

Department of Mechanical Engineering
University of Hartford
200 Bloomfield Avenue
West Hartford, CT 06117

Popular version of papers 2aSPa8 and 4aSP6
Presented Tuesday and Thursday morning, May 14 & 16, 2019
177th ASA Meeting, Louisville, KY

Introduction
In cities across the world everyday, people use and process acoustic alerts to safely interact in and amongst traffic; drivers listen for emergency sirens from police cars or fire engines, or the sounding of a horn to warn of an impending collision, while pedestrians listen for cars when crossing a road – a city is full of sounds with meaning, and these sounds make the city a safer place.

Future cities will see the large-scale deployment of (semi-) autonomous vehicles (AVs). AV technology is quickly becoming a reality, however, the manner in which AVs and other vehicles will coexist and communicate with one another is still unclear, especially during the prolonged period of mixed vehicles sharing the road. In particular, the manner in which Autonomous Vehicles can use acoustic cues to supplement their decision-making process is an area that needs development.

The research presented here aims to identify the meaning behind specific sounds in a city related to safety. We are developing methodologies to recognize and locate acoustic alerts in cities and use this information to inform the decision-making process of all road users, with particular emphasis on Autonomous Vehicles. Initially we aim to define a new set of audio-visual detection and localization tools to identify the location of a rapidly approaching emergency vehicle. In short we are trying to develop the ‘ears’ to complement the ‘eyes’ already present on autonomous vehicles.

Test Set-Up
For our initial tests we developed a low cost array consisting of two linear arrays of 4 MEMS microphones. The array was used in conjunction with a mobile robot equipped with visual sensors as shown in Fig. 1. Our array acquired acoustic signals that were analyzed to i) identify the presence of an emergency siren, and then ii) determine the location of the sound source (which was occasionally behind an obstacle). Initially our tests were conducted in the controlled setting of an anechoic chamber.

Picture 1: Test Robot with Acoustic Array

Step 1: Using convolutional neural networks for the detection of an emergency siren
Using advanced machine learning techniques, it has become possible to ‘teach’ a machine (or a vehicle) to recognize certain sounds. We used a deep layer Convolutional Neural Network (CNN) and trained it to recognize emergency sirens in real time, with 99.5% accuracy in test audio signals.

Step 2: Identifying the location of the source of the emergency siren
Once an emergency sound has been detected, it must be rapidly localized. This is a complex task in a city environment, due to moving sources, reflections from buildings, other noise sources, etc. However, by combining acoustic results with information acquired from the visual sensors already present on an autonomous vehicle, it will be possible to identify the location of a sound source. In our research, we modified an existing direction-of-arrival algorithm to report a number of sound source directions, arising from multiple reflections in the environment (i.e. every reflection is recognized as an individual source). These results can be combined with the 3D map of the area acquired from the robot’s visual sensors. A reverse ray tracing approach can then be used to triangulate the likely position of the source.

Picture 2: Example test results. Note in this test our array indicates a source at approximately 30o and another at approximately -60o.

Picture 3: Ray Trace Method. Note, by tracing the path of the estimated angles, both reflected and direct, the approximate source location can be triangulated.

Video explaining theory.

Kazuko Fuchi – kfuchi1@udayton.edu
University of Dayton Research Institute
300 College Park, Dayton, OH 45469

Andrew Gillman – andrew.gillman.1.ctr@us.af.mil
Alexander Pankonien – alexander.pankonien.1@us.af.mil
Philip Buskohl – philip.buskohl.1@us.af.mil
Air Force Research Laboratory
Wright-Patterson Air Force Base, OH 45433

Deanna Sessions – deanna.sessions@psu.edu
Gregory Huff – ghuff@psu.edu
Department of Electrical Engineering and Computer Science
Penn State University
207 Electrical Engineering West, University Park, PA 16802

Popular version of lecture: 1aSP1 Topology optimization of origami-inspired reconfigurable frequency selective surfaces
Presented Monday morning, 9:00 AM – 11:15 AM, May 13, 2019
177th ASA Meeting, Louisville, Kentucky

The use of mathematics and computer algorithms by origami artists has led to a renaissance of the art of origami in recent decades. Combining scientific tools with their imagination and artistic skills, these artists discover intricate origami designs that inspire expansive possibilities of the art form.

The intrigue of realizing incredibly complex creatures and exquisite patterns from a piece of paper has captured the attention of the scientific and engineering communities. Our research team and others in the engineering community wanted to make use of the language of origami, which gives us a natural way to navigate through complex geometric transformations through 2D (flat), 3D (folded) and 4D (folding motion) spaces. This beautiful language has enabled numerous innovative technologies including foldable and deployable satellites, self-folding medical devices and shape-changing robots.

Origami, as it turns out, is also useful in controlling how sound and radio waves travel. An electromagnetic device called an origami frequency selective surface for radio waves can be created by laser-scoring and folding a plastic sheet into a repeating pattern called a periodic tessellation and printing electrically conductive, copper decorations aligned with the pattern on the sheet (Figure 1). We have shown that this origami folded device can be used as a filter to block unwanted signals at a specific operating frequency. We can fold and unfold this device to tune the operating frequency, or we can design a device that can be folded, unfolded, bent and twisted into a complex surface shape without changing the operating frequency, all depending on the design of the folding and printing patterns. These findings encourage more research in origami-based innovative designs to accomplish demanding goals for radar, communication and sensor technologies.

Figure 1: Fabricated prototype of origami folded frequency selective surface made of a folded plastic sheet and copper prints, ready to be tested in an anechoic chamber – a room padded with radio-wave-absorbing foam pyramids.

Origami can be used to choreograph complex geometric rearrangements of the active components. In the case of our frequency selective surface, the folded plastic sheet acts as the medium that hosts the electrically active copper prints. As the sheet is folded, the copper prints fold and move relative to each other in a controlled manner. We used our theoretical knowledge along with insight gained from computer simulations to understand how the rearrangements impact the physics of the device’s working mechanism and to decide what designs to fabricate and test in the real world. In this, we attempt to imitate the origami artist’s magical creation of awe-inspiring art in the engineering domain.