1aAB7 – Drum fish spawning doesn’t miss a beat in the eye of a hurricane

Christopher R. Biggs – cbiggs@utexas.edu
Brad Erisman – berisman@utexas.edu

The University of Texas at Austin, Marine Science Institute
750 Channel View Drive,
Port Aransas, TX 78373

Popular version of paper 1aAB7
Presented Monday morning, November 5, 2018
176th ASA Meeting, Victoria, BC

Drum fish

Photo credit: Tyler Loughran

The location and frequency of spawning (reproduction) in fish has a direct effect on the abundance, stability, and resilience of a fish population. Major storm events, such as hurricanes, provide a natural experiment to test the ability of a fish population to withstand disturbances. Acoustic monitoring of Spotted Seatrout spawning revealed that these fish are extremely productive, spawning every day of the spawning season (April – September), including during a category 4 hurricane. These results illustrate the amazing resilience of estuarine fishes to intense disturbances and their potential to cope with projected increases in extreme weather events in the future.

Spotted Seatrout and many other species of “drum fish” make characteristic sounds during spawning (figure 1), which can be heard on underwater microphones, or hydrophones. This allows us to remotely monitor when fish spawn and how long they spawn for, which is especially helpful in murky water, where it is difficult to see. Seatrout spawning can be identified within the audio recordings by analyzing the intensity of the sound within the specific frequency range (250-500 Hz) of the Spotted Seatrout calls.

Figure 1. Recording of male Spotted Seatrout drumming sounds during spawning.

We monitored Spotted Seatrout spawning from April to September 2017 at 15 sites within the estuaries of South Texas, to see how changes in environmental conditions affected spawning. Our study also coincided with a category 4 hurricane. Hurricane Harvey made landfall 9 km east of Rockport, Texas on August 25, 2017 at 17:00 h CST. The eye of the storm was 28 km wide, maximum sustained winds were 59 m s-1 with gusts up to 65 m s-1, and the storm surge caused water levels to rise 3.8 meters above ground level.

The sound pressure level within the frequency range of seatrout spawning sounds peaked every evening between 20:00 and 21:00, indicating that spawning was occurring on a daily basis. During the hurricane wind-associated noise masked any potential spawning sounds, except at two stations that were directly in the path of the hurricane. When the eye of the storm was directly overhead those stations, wind-associated noise decreased, and spawning sounds were audible (figure 2). The time that spawning began shifted two hours earlier for five days after the storm, which may have been partly caused by the decrease in water temperature.

Figure 2. Spectrograms of recordings during Hurricane Harvey showing storm noise at 21:55 and seatrout chorusing at 22:25 within the 250-500 Hz bandwith (dotted lines).

Species that live and spawn in estuaries must deal with conditions that can change rapidly and unpredictably. It is important to understand how those changes impact spawning activity in order to maintain sustainable populations for the fishing industry. Further, understanding how fish respond to environmental disturbances in these environments may offer insight on how fish will respond to climate change and other human impacts elsewhere.

2pAB2 – Sound of wood-boring larvae and its automated detection

Alexander Sutin – asutin@stevens.edu
Alexander Yakubovskiy – ayakubov@stevens.edu
Hady Salloum – hsalloum@stevens.edu
Timothy Flynn – tflynn2@stevens.edu
Nikolay Sedunov – nsednov@stevens.edu
Stevens Institute of Technology, Hoboken, NJ 07030

Hannah Nadel – Hannah.Nadel@aphis.usda.gov
USDA APHIS PPQ S&T, 1398 West Truck Road, Buzzards Bay, MA 02542

Sindhu Krishnankutty – Sindhu.Krishnankutty@aphis.usda.gov
Department of Biology, Xavier University, Cincinnati, OH 45207

Popular version of paper 2pAB2 , “Sound of wood-boring larvae and its automated detection”
Presented Tuesday, May 8, 2018, 1:40-2:00 PM, LAKESHORE B,
175th ASA Meeting, Minneapolis.

larvae

Figure 1. Tree bolt with wood-boring beetle larva and attached sensors

The difficulty to detect potentially dangerous plant pests at ports of entry by agricultural inspectors, and the increasing invasion of U.S. agriculture and forestry by exotic pests in recent years are serious problems, given that the Federal Government instituted a robust reinforcement of the country’s borders and ports of entry. It is estimated that costs to the American economy caused by exotic invasive species are now over $138 B per year. Customs and Border Protection (CBP) facilitates processing of ~$2 trillion in legitimate trade, imports and exports yearly while enforcing U.S. trade laws that protect the economy, health, and the safety of people worldwide. Currently, CBP agriculture specialists inspect for pests relying on mostly manual techniques that are time-consuming and potentially not 100% effective because resources allow for only 2% of cargo to be examined.  Wood boring pests are especially time-consuming to detect, as they burrow and feed inside wood, and often leave few visual cues to their presence.

Stevens Institute of Technology has been investigating engineering solutions to augment the current wood inspection process at ports of entry in an effort to minimize the time spent per inspection and maximize the detection rate of infestations in wood packaging and wood products. One of our systems is based on the detection of vibrational pulses make by wood boring larvae during feeding; results of the initial research in this direction are presented in [1].  A major problem of automated detection of wood-boring larvae is detecting insect-induced vibrational pulses with noise in the background. To develop an acoustic-signature detection algorithm, numerous acoustic signals made by the larvae of Anoplophora glabripennis (Asian Longhorn Beetle) and Agrilus planipennis (Emerald Ash Borer) were collected in a quarantine facility at the USDA-APHIS PPQ Otis laboratory. We also recorded and analyzed typical background noise pulses, namely, speech, knocking, and tapping made by humans, and sounds of electronic equipment. Examples of time tracks and spectrograms of the recorded signal are shown in Fig. 2. and 3.

Figure 2. Recorded vibrational pulses from various sources.

Figure 3. Spectrograms of insect sounds and human speech.

In the conducted analyses, we considered the features of both sound pulses of larvae and typical noise pulses. The extracted and evaluated features of those sound pulses were based on the estimation of the duration, spectrum and spectrogram of the signal, and spectrum and spectrogram of the signal envelope (estimated via Hilbert transform). Some noise pulses (knocking and tapping) are longer than the larval bite sounds, while some (electronic beeps) are similar in duration. Speech includes fragments (vowels) which are much longer than larval bite sounds, but also very short fragments inside the vowels (high-pitched harmonics). The spectral content of some non-insect sounds differ from larval feeding sounds. The envelope spectra, therefore, appear to be informative features.

Analysis of the recorded vibrations allowed extraction of signal features that could ultimately be used for larval classification. These features include the main frequency of the generated pulses, their duration, and main frequency of the pulse envelop (modulation frequency).  In the conducted tests, these features show a clear separation of ALB and EAB acoustic signatures. For example, the main frequency of the ALB sound was in the range of 3.8-4.8 kHz, while for EAB it was between 1.2 and 1.8 kHz. A preliminary algorithm for automated insect-signal detection was developed. The algorithm automatically detects pulses with parameters typical for larva-induced sounds and rejects non-insect sound pulses that belong to the ambient noise. Detection is determined when the number of detected pulses for some time (1 min) exceeds the definite threshold.  In the test, this algorithm detected a larva in all samples without false alarms.

We are close to the finalization of a prototype for wood-boring-insect detection in wooden pallets. This prototype includes the following features:

  1. Sensitive sensors that are practically unaffected by external sounds. These sensors contain an accelerometer and a microphone used for ambient noise estimation and elimination of strong ambient noise signals that can penetrate the vibrational channel.
  2. An insect sound emitter to simulate real insect sounds and apply it to the testing and calibration of the detection system.
  3. Detection of insect-produced vibrations, based on the principles presented above.

Acknowledgement
This project was funded under contract with the U.S Department of Homeland Security’s Science and Technology Directorate (S&T). The opinions contained herein are those of the contractors and do not necessarily reflect those of DHS S&T.

References
[1] Sutin, A.,  T. Flynn, H. Salloum, N. Sedunov, Y. Sinelnikov, and H. Hull-Sanders. 2017. Vibro-acoustic methods of insect detection in agricultural shipments and wood packing materials. In: Proceedings of Technologies for Homeland Security (HST), IEEE International Symposium, 2017, Boston, USA, pp. 1 – 6.

[embeddoc url=”https://acoustics.org/wp-content/uploads/2018/05/Sutin-LLP.docx” viewer=”microsoft”]

1pAB4 – Size Matters To Engineers, But Not To Bats

Rolf Müller – rolf.mueller@vt.edu
Bryan D. Todd

Popular version of paper 1pAB4, “Beamwidth in bat biosonar and man-made sonar”
Presented Monday, May 7, 2018, 1:30-3:50 PM, LAKESHORE B,
175th ASA Meeting, Minneapolis.

Bats and Navy engineers both use sonar systems. But do they worry about the same design features?

To find out, we have done an exhaustive review of both kinds of sonar systems, poring over the spec sheets of about two dozen engineered sonars for a variety of applications and using computer models to predict 151 functional characteristics of bat biosonar systems spanning eight different biological families. Crunching the numbers revealed profound differences between the way engineers approach sonar and the way bats do.

The most important finding from this analysis is related to a parameter called beamwidth. Beamwidth is a measure of the angle over which the emitted sonic power or receiver sensitivity is distributed. A small beamwidth implies a focused emission, where the sound energy is – ideally – concentrated with laser-like precision. But the ability to generate such a narrow beam is limited by the sonar system’s size: the larger the emitter is relative to the wavelength it uses, the finer the beam it can produce. Reviewing the design of man-made sonars indicates that beamwidth has clearly been the holy grail of sonar engineering — and in fact, the beamwidth of these systems hews closely to their theoretical minima.

bats

Some of the random emission baffles made from crumpled aluminum foil that served as a reference for the scatter seen in the bat beam width data.

But when it comes to beamwidth, tiny bats are at a significant disadvantage: even the largest bat ears are barely ten times the size of the animals’ ultrasonic wavelength, while engineered systems can exceed their wavelengths by 100 or 1000 times. Remarkably, our analysis showed that bats seem to disregard beamwidth entirely. In our data set, the bats’ beamwidth scattered widely towards larger values; the scatter was even larger than that for random cone shapes we created from crumpled aluminum foil. Clearly, the bats’ sonar systems are not optimized for beamwidth. But we know that they are incredible capable when it comes to navigating complex environments — which begs the question: what criteria are influencing their design?

We don’t know yet. But the bats’ superior performance demonstrates every night that giant sonar arrays with narrow beamwidths aren’t the only and certainly not the most efficient path to success: smaller, leaner solutions exist. And those solutions will be necessary for compact modern systems like autonomous underwater or aerial vehicles. To make sonar-based autonomy in natural environments a reality, engineers should let go of their fixation on size and look at the bats.

3aAB8 – Sea turtles are silent… until there is something important to communicate: first sound recording of a sea turtle

Amaury Cordero-Tapia  – acordero@cibnor.mx
Eduardo Romero-Vivas – evivas@cibnor.mx
CIBNOR
Mar Bermejo 195
Playa Palo de Santa Rita Sur 23090
La Paz, BCS, Mexico

Popular version of paper 3aAB8, “Opportunistic underwater recording of what might be a distress call of Chelonya mydas agassizii”
Presented Wednesday morning, December 6, 2017, 10:15-10:30 AM, Salon F/G/H
174th ASA Meeting, New Orleans, Louisiana
Click here to read the abstract.

Sea turtles are considered “the least vocal of all living reptiles” (DOSIT), since their vocalization has been documented only during nesting (Cook & Forrest, 2005). Although they distribute worldwide in the oceans, there seems to be no recordings of sounds produced by them, perhaps until now.

In Baja California Sur Mexico there is a conservation program run by Government Authorities, Industry, and Non-Governmental Agencies focused on vulnerable, threatened and endangered marine species. In zones of high density of sea turtles, special nets, which allow them to surface for breathing, are deployed monthly for monitoring purposes. Nets are checked by divers every 2 hours during the 24 Hrs. of the census.

During one of these checks a female specimen of Green Turtle (Chelonya mydas agassizii) was video recorded using an action cam. Posterior analysis of the underwater recording showed a clear pattern of pulsed sound when the diver was at close proximity to the turtle. The signal covers the reported audition range for this species (Ketten & Bartol, 2005; Romero-vivas & Cordero-Tapia, 2008) and given the circumstances we think that it might be a distress call. With more recordings we will confirm if such is the case, although this first recording gives an initial hint of what to look for. Maybe sea turtles are not that silent; there was just no need to break the silence

Figure 1. Green turtle in the special net & sound recording

 

Dosits.org. (2017). DOSITS: How do sea turtles hear?. [online] Available at: http://dosits.org/animals/sound-reception/how-do-sea-turtles-hear/ [Accessed 16 Nov 2017].

Cook, S. L., and T. G. Forrest. 2005, Sounds produced by nesting Leatherback sea turtles (Dermochelys coriacea). Herpetological Review 36:387–390.

Ketten, D.R. and Bartol, S.M. 2005, Functional Measures of Sea Turtle Hearing. Woods Hole Oceanographic Institution: ONR Award No: N00014-02-1-0510.

Romero-Vivas, E. and Cordero-Tapia, A. 2008, Behavioral Acoustic Response of two endangered sea turtle species: Chelonia Mydas Agassizzi –Tortuga Prieta- and Lepidochelys Olivaceas –Tortuga Golfina- XV Mexican International Congress on Acoustics, Taxco 380-385.

 

 

 

 

 

4aAB4 – Analysis of bats’ gaze and flight control based on the estimation of their echolocated points with time-domain acoustic simulation

Taito Banda – dmq1001@mail4.doshiha.ac.jp
Miwa Sumiya – miwa1804@gmail.com
Yuya Yamamoto – dmq1050@mail4.doshisha.ac.jp
Yasufumi Yamada – yasufumi.yamada@gmail.com
Faculty of Life and Medical Sciences, Doshisha UniversityKyotanabe, Kyoto, Japan

Yoshiki Nagatani – nagatani@ultrasonics.jp
Department of Electronics, Kobe City College of Technology, Kobe, Japan.

Hiroshi Araki – Araki.Hiroshi@ak.MitsubishiElectric.co.jp
Advanced Technology R&D Center, Mitsubishi Electric Corporation, Amagaski, Japan

Kohta I. Kobayasi – kkobayas@mail.doshisha.ac.jp
Shizuko Hiryu – shiryu@mail.doshisha.ac.jp
Faculty of Life and Medical Sciences, Doshisha University, Kyotanabe, Kyoto, Japan

Popular version of paper 4aAB4 “Analysis of bats’ gaze and flight control based on the estimation of their echolocated points with time-domain acoustic simulation.”
Presented Friday morning, December 7, 2017, 8:45-9:00 AM, Salon F/G/H
174th ASA in New Orleans

Bats broadcast ultrasound and listen to the echoes to understand surrounding information. It is called echolocation. By analyzing those echoes, i.e., arrival time of echoes, bats can detect the position of objects, shape or texture [1-3]. Contrary to the way people use visual information, bats use the sound for sensing the world. How is the world different between the two by sensing? Because both senses are completely different, we cannot imagine how bats see the world.

To address this question, we simulated the echoes arriving at the bats during obstacle-avoiding flight based on the behavioral data so that we could investigate how the surrounding objects were described acoustically.

First, we arranged microphone arrays (24 microphones) and two high-speed cameras in an experimental flight chamber (Figure 1) [4]. The timing, positions and directions of emitted ultrasound as well as the flight paths were measured. A small telemetry-microphone was attached on the back of the bat so that the intensity of emitted ultrasound could be recorded accurately [5]. The bat was forced to follow a S-shaped flight pattern to avoid the obstacle acrylic boards.

Based on those behavioral data, we simulated propagation of sounds with the measured strength and direction emitted at the position of the bat in the experiment, and we could obtain echoes reaching both left and right ears from the obstacles. By using interaural time difference of echoes, we could acoustically identify the echolocated points in the space for all emissions (square plots in Fig.2). We also investigated how the bats show spatial and temporal changes in the echolocated points in the space as they became familiar with the space (top and bottom panels).

We analyzed changes in the echolocated points by using this acoustic simulation, corresponding to which part of objects the bats intended to gaze at. In a comparison between before and after the habituation in the same obstacle layout, there are differences in the wideness of echolocated points on the objects. By flying the same layout repeatedly, false detection of objects was reduced, and their echolocating fields became narrower.

It is natural for animals to pay their attention toward objects adequately and adapt both flight and sensing controls cooperatively as they became familiar with the space. These finding suggests that our approach in this paper, i.e., acoustic simulation based on behavioral experiment is one of effective ways to visualize how the object groups are acoustically structured and represented in the space for bats by echolocation during flight. We believe that it might serve a tip to the question; “What is it like to see as a bat?”

ehcolocation
Figure 1 Diagram of bat flight experiment. Blue and red circles indicate microphones on the wall and the acrylic boards, respectively. Two high-speed video cameras are attached at the two corners of the room. Three acrylic boards are arranged to make bats follow S-shaped flight pattern to avoid the obstacles.

echolocation
Figure 2 Comparison of echolocated points between before and after space habituation. The measured positions where the bat emitted the sound are shown with circle plots meanwhile the calculated echolocated points are shown with square plots. Color variation from blue to red for both circle and square plots corresponds to temporal sequence of the flight. Sizes of circle and square plots correspond to the strength of emissions and their echoes from the obstacles at the bat, respectively.

References:
[1] Griffim D. R., Listning in the dark, Yle University, New Haven, CT, 1958

[2] Simmons J.A., Echolocation in bats: signal processing of echoes for target range, Science, vol. 171, pp.925-928., 1971

[3] Kick S. A., Target-Detection by the Echolocating Bat, Eptesicus fuscus, J Comp Physiol, A., vol. 145, pp.431-435, 1982

[4] Matsuta N, Hiryu S, Fujioka E, Yamada Y, Riquimaroux H, Watanabe Y., Adaptive beam-width control of echolocation sounds by CF-FM bats, Rhinolophus ferrumequinum nippon, during prey-capture flight, J Exp Biol., vol. 206, pp.1210-1218, 2013

[5] Hiryu S, Shiori Y, Hosokawa T, Riquimaroux H, Watanabe Y., On-board telemetry of emitted sounds from free-flying bats: compensation for velocity and distance stabilizes echo frequency and amplitude, J Comp Physiol A., vol. 194, pp.841-851, 2008

2aAB3 – A Loud, Ultrasonic Party

Quantifying complex bat calls to understand how bats echolocate in groups

Contact: Yanqing Fu, yfu@saintmarys.edu

Yanqing Fu, Laura N. Kloepper
Department of Biology, Saint Mary’s College,
Notre Dame, IN 46556

Popular version of paper 2aAB3, “First harmonic shape analysis of Brazilian free-tailed bat calls during emergence.”
Presented Monday morning, June 26, 2017
173rdASA Meeting, Boston

Imagine you are at a party. The music is loud and lots of people are talking. How can you hear your voice and those of other people? Similarly, bats face this problem when in groups. When a single bat uses echolocation, it emits an ultrasonic call (above 20 kHz) and extracts environmental information by analyzing echoes.

But for bats that live and travel in large groups, echolocation should be challenging. Under these circumstances, they should encounter the problem of sonar jamming, where they might have a hard time distinguishing their echoes from other bats’ and their own calls. One bat species that is known for extreme grouping is the Brazilian free-tailed bat, Tadarida brasiliensis.

bats

Figure 1: Brazilian free-tailed bat (Tadarida brasiliensis) emergence, which usually occurs from 16:00 to 20:00, last about 15 minutes.

These bats can quickly change characteristics of their calls when probing different environments and performing different tasks. These call characteristics include duration, repetition rate, and frequency (or pitch). The shape of a call, how the call changes frequency over time, may provide important echo information to the bat.

Brazilian free-tailed bats can change the call shape from a straight line (constant frequency) to a downward curved line (nonlinear frequency modulation) and finally an inclined line (linear frequency modulation) within milliseconds (Fig. 2). Additionally, the bats can emit different frequency components at the same time. The call shape variation of bats flying in a group might help us to understand how they avoid sonar jamming.

 

Figure 2: Typical call shapes of Brazilian free-tailed bat (Tadarida brasiliensis).

In order to investigate how these bats change calls while flying in groups, we developed a new method to identify the shape of a bat call and quantitatively compare different call shapes. This method separates the multiple frequency components of bat calls (called harmonics) and tracks the trend of frequency over time using advanced digital signal processing techniques (Fig. 3).

Once these trends are extracted, call shapes can be quantitatively compared through point-to-point comparison by aligning different call durations. This method is the first important step to understanding how bats avoid sonar jamming while in large groups. We hypothesize that some call shapes are more robust to distinguish than others when in a chaotic sound environment.

Figure 3: Typical procedures for the isolation and tracking the first frequency component of the Brazilian free-tailed bat echolocation call. (a) Original echolocation call; (b) low frequency noise free call; (c) noise among different frequency components was removed; (d) isolated clean first frequency component; (e) call shape was extracted, black solid line superimposed on the isolated frequency component.