An Elephant Infrasound Detection IoT Device and the Data Analysis System to Build Elephant Behavioral Detection

Oxford Group 2 Project 15 (AI for Good) Presentation Repository

Authors:

Aneeq Rehman, Dnyaneshwar Kulkarni, Giulia Ciardi, Tommaso Pappagallo, Vivek Shaw, Shawn Deggans

Elephant walking with spectrogram in the background.

Welcome to Group 2's AI for Good presentation. We were honored with the opportunity to contribute to Microsoft's Project15 through University of Oxford. This was a fantastic learning experience. We not only learned of the plight of elephants, but that we could share our knowledge to help save these amazing animals.

Project 15 Group 2 AI For Good Presentation

(Objectives) Can We Use IoT and Machine Learning to Save Elephant Lives?

The goal of this project is to create a platform that will allow researchers to continuously build and refine machine learning models that recognize elephant behavior from infrasound. This platform is based on Microsoft's Project15 architecture with modifications to meet the stated goal of the system. This document will explore the problem area we are attempting to address, and the system design we think will best address the needs of that problem area.

Our recommended system is evolutionary and allows researchers to use the modern Agile development practices of empiricists decision-making, continuous iteration, and continuous delivery to bring the behavioral categorization models closer to the elephants in the field. It is the hope of this project's participants that these models and the system designed to build these models will help save the lives of elephants from human influenced threats.

(Background) Human Influenced Threats and the Plight of Elephants

"We lose one elephants from the planet every fifteen minutes," says Sarah M. W. Maston, IoT Solutions Architect. "We will lose all elephants in ten years."

Elephant populations are in decline¹.

In a single decade between 1979 and 1989, half of all Africa's elephants were lost to the ivory trade, according to pan African census conducted by STE (Save the Elephants) lain Douglas-Hamilton. In response to this decline, CITES (the Convention on International Trade in Endangered Species of Wild Fauna and Flora) banned international trade of elephant ivory². This ban provided a temporary reprieve from the population decline of elephants until demand for ivory came from the new wealth of China and other wealthy nations. The continued demand for ivory is one of the primary factors that contributes to the decline of the elephants' population³.

However, the demand for ivory aren't the only challenges to elephant populations. According to elephantvoices.org, the survival and wellbeing of elephants are threatened by:

• Escalating poaching, or illegal killing, for the commercial trade in ivory and meat.

• Growing demands of exploding human populations and poverty.

• Increasing loss and fragmentation of natural habitats and lack of land-use planning.

• Rising conflict with humans over diminishing resources⁴.

In the book, Monitoring Elephant Population and Assessing Threats a manual for researchers, managers, and conservationists, author Simon Hedges states, "Elephants still occur in isolated populations across much of their historical range, but unfortunately their numbers are rapidly declining. The major threats to the continued survival of these species in many places are habitat loss, degradation, and fragmentation, as well as poaching for ivory and other forms of illegal killing or capture—usually because of conflicts with humans. Effective monitoring programs, which involve systematic collection of data on the distribution, size and trend of elephant populations, as well as threats such as illegal killing, are needed to provide a rational basis for the management of elephant populations⁵.

Microsoft's Project15 is an open-source solution platform built to aid conservation projects using IoT. We aim to apply this technology stack to the tracking and understanding of elephant behavior to contribute to an effective monitoring program. We believe our solution will help do the following:

• Systematically collect data related to elephant vocalization.

• Detect threats based on elephant behavior.

Focusing specifically on recognizing elephant vocalization using infrasound telemetry, we will demonstrate how a research team could utilize machine learning to improve our understanding of elephant communication in relation to their environment.

(Assumptions) Building on What We Know and Understand

We know quite a lot about elephant behavior and communication. Elephants have demonstrated insight and complex problem-solving skills, as demonstrated in the video below.

"Insightful Problem Solving in an Asian Elephant. Kandula, an 8-year old Asian elephant at the Smithsonian's National Zoo, demonstrates insightful problem solving by positioning a large cube under a treat that is too high for him to reach. By standing on the cube he can reach the food. He was not trained to do this and came up with this solution on his own. From the scientific paper "Insightful Problem Solving in an Asian Elephant," published Aug. 18, 2011 in the journal PLoS ONE."

Kandula, National Zoo, insightful problem solving 2 YouTube Video

Elephants show complex social skills and behavior*.* They show self-recognition⁶. They demonstrate an evolving pattern of behaviors when faced with complex problems⁷.

The research shows that elephants are complex, intelligent animals with a rich social familial structure. It's our assumption that these behaviors are a valuable tool in the struggle to save elephants lives. To accomplish our goal of building a system that focuses on this aspect of elephant life and behaviors, we turned to existing research and projects that focused on infrasound and elephants. A primary source for research is The Cornell Lab Elephant Listening Project.

The Elephant Listening Project is built on the work carried out by Katy Payne, a zoologist and researcher who first used infrasound to listen in on the conversations of some of Earth's largest mammals, like whales and elephants⁸.

We are focusing on infrasound, because like Katy Payne we hope to not only build a greater understanding of elephant conversations, but the behavior that goes with those conversations.

Infrasound

Infrasound YouTube Video

~ Infrasound

It's important to understand that the elephant's vocalization is produced using its complex larynx. Elephants' low-frequency vocalizations are produced by flow-induced self-sustaining oscillations of laryngeal tissue⁹. This unique anatomy allows elephants to communicate in a sonic range that is below our hearing (14-24hz). This means that a standard microphone will not be an adequate detector for elephant vocalization. The normal range for standard microphones is 20hz-20khz. Specialized microphones can capture sound in the range of 1hz and above. Acquiring these specialized microphones and infrasound detection tools are beyond the scope of this project, in our approach we will outline how such a tool could be created and used in the field by rangers and researchers.

As we explored infrasound, we learned that many sounds in the forest were in the infrasound range. One of the challenges of capturing elephant communication was learning to recognize it among the many other sounds of the forest.

To narrow down the elephant sounds, it's important to understand how they communicate and the differences between male vocalizations and female vocalizations.

Elephants produce a wide range of sounds from very low frequency rumbles to higher frequency snorts, barks, roars, cries, and other idiosyncratic calls¹⁰.

Because there is already research around call types (https://www.nationalgeographic.com/animals/article/what-elephant-calls-mean) we can start with this as the basis of our models.

With an understanding of these communication details, and evidence that suggests elephants might have a specific way of addressing threats, we believe that using these vocalization patterns will allow us to recognize the specific rumbles of elephants and separate them from other low frequency noise¹¹.

Because so much research has been conducted in this area, we believe this is a good area to focus our value creation efforts. We believe a system that allows for the collection of infrasound, the separation of elephant vocalizations from other low frequency noise, and categorization based on continuous machine learning, all built on Microsoft's Project15 open-sourced infrastructure using Azure DevOps for CI/CD will provide researchers, rangers, and conservationists a platform to allow for better elephant behavior understanding.

Our Proposed Methodology for Achieving an Elephant Behavior Detection System

Our system is designed based on the patterns Microsoft has introduced in its Azure IoT Suite of tools and services. The basic structure of that architecture is divided into three primary areas of focus:

• Things

• Insights

• Action

Azure IoT Reference Architecture

Figure - Azure IoT Reference Architecture

Comparing this architecture to the Project15 open-source platform for conservation, we can see the overlap.

Project 15 Open Platform Architecture

Figure - Project 15 Open Platform Architecture

We made a few early design decisions related to the devices (the things). We wanted something less intrusive to the elephants than a collar or wearable. The device we are proposing is one that managed in a grid configuration.

There are good arguments to be made for developing a device that the elephant "wears." For instance, one of the easiest ways to detect the vocalizations of an elephant would be to use a vibration sensor. This vibration sensor could be built using piezoelectric ceramic to detect acoustic waves and worn on the elephant's trunk. Special tracking collars are in use today, but this solution seems intrusive to the animal and difficult to manage and maintain for the limited rangers responsible for the well-being of the elephants. We wanted something to capture a larger range.

The Things – Infrasound Capture Devices

We are interested in exploring one type of infrasound capture device:

• Infrasound detection array

The infrastructure detection array is the capstone project of a Spring 2020 project by team members: James Berger, Tiffany Graham, Josh Wewerka, and Tyler Wyse. Project details can be explored here, "https://engineering.ucdenver.edu/current-students/capstone-expo/archived-expos/spring-2020/electrical-engineering/elec6-improving-infrasound-detection"

This project was designed to detect infrasound direction using an array of infrasound microphones, but we would use the infrasound microphones in a low-powered device that could communicate over a long-range radio network, such as LoRaWAN. This network of devices would serve as the primary means for tracking elephants and recording their vocalization.

We envision this device as an IoT Edge device capable of supporting Azure Storage Account Blob Storage and custom IoT Edge Modules that are containerized machine learning modules capable to converting audio into spectrograms, and spectrograms into bounding box marked images, and delivering telemetry to IoT Hub.

This was our initial device design:

Group 2 Initial Infrasound Capture Concept

Figure - Group 2 Original Infrasound Capture Concept

As we completed the proof-of-concept code for this project, we came up with this more detailed diagram based on our known logic flow:

Group 2 matured infrasound detection IoT Edge Device

Figure - Our Matured Infrasound Detection IoT Edge Device

The above follows the overall proposed flow of the device-to-cloud communication, as well as the IoT Edge's internal device logic.

1. The infrasound capture device continuously monitors for infrasound. These infrasound signals are captured and sent to the device for analysis.

2. The 64-bit Raspberry Pi model captures the infrasound audio signals, breaks these into segments based on detected beginning-of-signal and end-of-signal indicators. Initially this limitation could mean we miss "conversations," because this is the equivalent of capturing a word from each sentence in human speech. Elephants don't necessarily speak in words and sentences, so this likely won't keep us from meeting the goals of the system.

3. These audio files will be saved to an Azure blob storage device module in a audio clips file [https://docs.microsoft.com/en-us/azure/iot-edge/how-to-store-data-blob?view=iotedge-2018-06].

4. We'll use an Azure Function to serve as a file watcher for the file storage. When it detects the file is there, it will convert it to a spectrogram. [See spectrogram conversion process].[See Wren file watcher solution: https://jaredrhodes.com/2017/11/27/create-azure-function-to-process-iot-hub-file-upload/]. When audio files are processed, they are converted to spectrograms. These spectrograms are saved to an image inbox.

5. A second function listens to the image inbox for new images. When new images appear, the function feeds the images to the classification model. The classification model returns the bounding box coordinates. We'll use CV2 to draw the coordinates on the image. Images are then saved to Bounding Box Images folder for more backend processing within Azure ML to help feed the ML loop.

The above covers the primary 'things' portion of the architecture. The next section covers the insights portion of development where we capture what's reported for telemetry to IoT hub and captured into the Azure Data Lake Gen 2.

The Insights and Actions - Telemetry, Images, and Coordinate Data

Our backend IoT architecture is based on a few key functionalities needed to meet the requirements of our minimal value product. There are a few things we've left out of the typical Azure IoT Suite. We have not included the Device Provisioning Service, because we do not anticipate deploying many devices. We aren't including Logic Apps for business processing because we anticipate this being a closed loop system. The Actions portion of the Things, Insights, Actions formula will be handled using Azure ML, VS Code, and Storage Explorer for management and administration.

1. IoT Hub is used as the primary IoT gateway. From here we manage device provisioning, IoT edge device creation, telemetry routing, and security. We will use the SAS token security and employ a regular SAS key rotation policy to keep devices secure. We will directly attach the [2] blob storage account to store images from the Azure IoT Blob Storage Module. Azure IoT accepts updated docker file images from the [9] Container Registry. Telemetry data is transferred to the [3] Stream Analytics Jobs.

2. Blob Storage holds images saved on the IoT Edge Module. This means that it also has folders that represent audio clips, image inbox, and bounding box images. This represents cold storage of data that could be studied later or needs to be archived.

3. Stream Analytics Jobs are used to query telemetry data and run business rules over streaming. We will use this to format our telemetry data and enrich it as needed. Data will be stored in the [4] Azure Data Lake Storage Gen 2.

4. Azure Data Lake Storage Gen 2. We will use a standard data engineering processing layers within the data lake that includes bronze, silver, and gold. Data coming directly from the [3] Stream Analytics Jobs will land directly in the bronze container within a date-time folder structure.

5. Azure ML is the primary workspace for testing and training machine learning models. We're anticipating two primary models. One built specifically for recognizing infrasound from audio files and another to specifically create bounding boxes around infrasound that best represent elephant vocalizations [Aneeq or Tommaso please correct this or clarify if wrong]. When models are complete, they are exported as containers and checked into [8] DevOps for the CI/CD process.

6. Visual Studio Code or Code is the primary tool for managing the IoT Edge module code logic related to working with device blob storage and data orchestration. Code from here is checked into [8] DevOps and is built out to the [9] Container Registry.

7. Storage Explorer is used to help manage data on the [2] Blob Storage and [4] Data Lake Storage Gen 2. This allows for testing, downloading, or moving data for an operations person.

8. Azure DevOps is one of the most important pieces of our MLOPs process. From here code is checked in and pushed out to the [9] Container Registry. There are two primary types of modules that can be created. There are code modules that represent the Azure Functions and ML model data orchestration. Then there are ML models. Model modules can be created from [5] Azure ML and checked in to be deployed to the [9] Container Registry. Basically, code is typically containerized in Azure DevOps for deployment to the [9] Container Registry and [1] IoT Hub is triggered to load the latest version of the latest docker file.

9. Container Registry is used to hold the Dockerfile images that represent the IoT Edge Modules. Modules are pulled by [1] IoT Hub to automatically deploy to devices in the field.

Observations

1. Most of our initial observations were around the lack of labelling on the sound files we worked with. It took some effort to determine what were elephant sounds VS other possible infrasound files. To better recognize elephant vocalizations, creating a dataset of labeled data would help improve model training efforts related to recognizing elephant behavior.

2. After research, we understand that our approach of analyzing infrasound is a limited scope of the overall communication among elephants. The sounds are important, but like humans, elephants express their communication in more ways than just their voices. Elephants also express body language (visual communication), chemical communication, and tactile communication. We understand that this means the audio only component won't get the full picture, but we do hope that it will add Elephant Sound Database.

3. There are very few quality tools for capturing or measuring infrasound on the market. Most range from either very expensive scientifically exact measuring tools meant for detecting Earthquakes and nuclear tests or very cheap Do-it-yourself electronic hobbyist attempts, like the device we've proposed here. We did discover that there are mobile apps that could serve as a less expensive alternatives, but they require a solid internet connection either through WiFi or a 3-4G mobile connection. Considering these are in remote locations, we didn't think that would be a viable solution. What we have proposed doesn't quite exists yet, so there's opportunity to help by creating a very small, very low power device that could possible user either UHF band to communicate or LoRaWAN. It might also be possible to attach the detector to drones. The drones could fly to a location, land, and shut off, and then collect data. After collecting data, they could power back up and fly to a point where they could deliver the data to a network. This was beyond the scope of our legwork for this project, but it does represent what's possible using an IoT Edge device that can work in an offline state.

4. There is a real conflict between humans invading elephant spaces and elephants invading human spaces. Elephants are the cause of lots of crop damage and farmer deaths in India. Technologies that make farmers aware of the elephants might be a good idea to help protect crops and human life. However, that technology could be used to endanger elephant lives. This is an interesting problem area that we didn't explore. Finding a solution in this area that considers the protection of human lives and property as well as the lives of elephants would be a problem worth solving.

Analysis - A Demo of a Proof of Concept

Building the complete solution was outside the scope of time we had available. Therefore, we decided to provide a collection of technical proofs that we felt best represented the viability of this concept. Here were the primary areas where we want to build technical solutions to address specific problem areas.

Proof #1 - We could use DevOps to produce our IoT Suite of services and automatically push new IoT Edge modules to an IoT Edge device.

Group 2 IoT Edge Pipeline

Our goal was to build a complete MLOPs pipeline, but we made a few early decisions that made that difficult. We trained and created some of our models in vendor locked systems. This limited our ability to create a fully functioning MLOPs process, but we were able to build a standard IoT Edge DevOps pipeline. This means that any custom modules checked in could be automatically deployed to the edge device. The following link will take you to a YouTube video explaining it.

A YouTube video walkthrough of our DevOps process.

Proof #2 - We could build out the Azure services and infrastructure needed to support our ideas.

A YouTube video walkthrough of our Azure infrastructure.

Proof #3 - We could read sound files, convert those to spectrograms, and use those spectrograms to recognize elephant vocalization.

Four our spectrogram generation we implemented the MATLAB software as it enabled higher fidelity power spectrum imaging improving the performance of the object detection phase due to smoothening over more granular bands in both frequency and time something which was difficult to achieve with python libraries that we tested out extensively including librosa, scipy etc which required a tradeoff to be made.

For our feature extraction we calculated 19 different variables with relation to flow statistic on the audio (mean, variance, etc.) and common signal characteristics (rms, peak2peak, etc.) making use of the MATLAB software here which enabled a wider array of spectral features to be extracted. In summary for each sample, we found the frequency of the peak magnitude across the overall power spectrum, the 1st formant, the 2nd formant, the max, min, mean and finish of the fundamental. In addition, we computed the power in the low frequency range 0-30 Hz, mid frequency range 30-120 Hz and high frequency range >120 Hz. We located the frequency of the spectral kurtosis peak and the flow cumulative sum range.

Our results offer some starting points for further research particularly in relation to trying to learn any underlying structure, with regards to age groups, in our population, given unlabeled data. We note that two independent approaches overlap in their age group classifications for just over 75% of the data meaning that our hypothesis that the wav files can be separated, and do in fact cluster, into two groups separable by age might hold true. This offers two potential tentative methods to begin further labelling the wav files by maturity group (i.e.: A vs B).

A brief explanation of the two methods follows. In the first method we implemented the thresholds proposed in (https://www.nature.com/articles/srep27585/tables/3) by calculating the 1st,2nd Formant, Max, Min, and Finish Frequencies for each sample and using these to classify the samples into Maturity Group 1 or 2. In the second method we implemented K-means clustering and PCA dimensionality reduction (dimension equals to 2) to classify the points into two clusters representing either maturity group. This method showed clear signs of clustering when the points were projected into the PCA dimensions as seen in the output below.  

When comparing method 1 and 2 the classified maturity groups overlapped for over 75% of the data, however, to check whether this is statistically significant we would need to perform a t-test.

Proof #4 - We could produce the primary logic that represents the inner workings of the IoT Edge device. This means we could take the spectrogram image and categorize the elephant vocalizations using a machine learning model, mark that image with a bounding box representing that vocalization, and send the data related to these findings to IoT Hub as telemetry.

A YouTube video walkthrough of our Bounding box and Prediction logic.

Discussions - Some of our Team's Thoughts on this Project

Our initial conversations in late December and early January were primarily related to the datasets. Questions arose around what data we should review, how we should process audio files, and even how we should store and retrieve the data for review.

Our initial idea was to tackle the demographics problem, but there were questions around recognizing individuals. We didn’t feel we had enough information labelling individual elephants. “the biggest issue I think is that whilst we might be able to find out the optimal number of clusters if we don't know anything about the individuals who made those sounds then it will be difficult to say much about their demographics. "one must remember that this analysis was done without knowing which individual gave which call nor what the individual’s specific behavior was."

It was around the end of December that we began discussing setting up MLOPs.

It was on the 5th that we had our first MATLAB produced rumble.

This was discovered by using the following article: https://www.mathworks.com/help/signal/ug/practical-introduction-to-time-frequency-analysis.html

Overall, we started seeing much better results than the various Python libraries we had initially tested. The MATLAB libraries were too good not to continue to develop with, despite that it locked us into a specific vendor and made it impossible for us to use the libraries in an IoT Edge container. This was due to licensing restrictions. The hope would be that because this is a proof-of-concept, we could develop around our limitations until a license could be secured.

This is what the code looked like:

Our calculation of the 1st and 2nd formant implemented approaches commonly utilized in human language processing which might not translate to animal language processing so guidance in setting the parameters here would ensure more accurate approximations. Other helpful areas. This Kaggle link for pitch detection: https://www.kaggle.com/lrthtn/pitch-detection

It was around mid-January that we began to discuss what the elephant detection device would look like. This was when we created the first diagram shown above.

It was also around this time that we had fully shifted away from demographic data and started focusing more on capturing behavior changes. Basically, the elephant formant looked to be a key in helping determine true elephant rumbles from other noise. “These rumbles exhibited an upward shift in the second formant location, which implies active vocal tract modulation, compared to rumbles made in response to white noise playbacks. In a second experiment, audio playbacks of these rumbles produced in response to bees elicited increased headshaking, and further and faster retreat behavior in other elephants, compared to control rumble playbacks with lower second formant frequencies.”

From our research we found additional evidence that we might be on the right trail, “While formants were still distinctive, absolute frequency values as well as shape-related features of the fundamental frequency differed between individuals. This is similar to the discriminative parameters found in female rumbles". It's basically saying that other spectral features like the fundamental and absolute freq values vary too much between individuals to be indicators of demographics or behaviour ( a bit like how we all have different sized feet)”

[1/11 1:36 AM] Tommaso Pappagallo (Guest)

Had an idea regarding the formant. We can calculate the slope of the formant plots over discrete consecutive time points and if the average is positive then we classify the signal as a 1 (stress) and if zero or negative as a 0 (not stress). With this we can do a logistic regression over a range of other variables that describe the spectral shape (specteal kurtosis etc) to have a model that can classify any new data point as 1 or 0. I think that could work what do you think will try that today Aneeq Rehman (Guest)

It was here that we had made the decision as a team to focus on the path of elephant behavior, but with a focus on detecting rumbles from the audio samples we had access to.

As we continued through the project, we determined that building some type of proof-of-concept that displayed the primary intent of our project would be ideal. We knew we didn’t have time to create an infrasound detector, an audio wave converter, and classifier all on one device. However, we worked to prove out as many individual parts as possible.

Conclusions and Moving Forward

There's a lot of opportunity for addressing this problem with technology. It's our opinion that the solutions should be respectful of the elephants, their environment, and their social structures. We've seen studies that require the elephants in captivity, or required elephants to wear devices, or require cumbersome devices in their environment.

We do understand that trade-offs are sometimes necessary to save their lives, but we would like to secure the lives of the elephants in a way that is respectful of their lives and environment. Meaning, if we can remain an invisible observer that only intercedes to save lives from human encroachment, that would be ideal.

We also understand that our offering is only a small piece of the larger set of tools required to do an adequate job of saving elephant lives. Our solution is modular and could easily integrate with other solutions like satellite imaging, drone imaging, infrasound and other audio triangulation devices, thermal scanners, and other telemetry gathering devices that can work seamlessly with the environment.

Our hope would be that we can complete the development necessary to see a working version of this system. We've tested many of the individual parts, tested some of the integrations, but it's still not in a completed state. We would like to build Infrastructure as Code versions of the Azure infrastructure, check in all the IoT Edge code in Azure DevOps, and being developing our models in Azure ML. Additionally, there are some tools that require professional licenses to complete. We would need to either purchase those licenses or find other libraries that offer similar or same functionality. The next step would be developing the actual IoT Edge device and finally we would like to see that device deployed and used by the researchers and rangers involved in the protection of elephants.

Footnotes

---

What's included in this project?

This project includes everything we tested and experimented with to produce the rumble classifier, the machine learning pipeline, and the IoT Edge DevOps project.

Because the project is still a work in progress, it's not ready to build as a single solution. However, there are a number of pieces that we feel would be interesting to developers interested in contributing to the solution.

Some portions of the solution require a licensed version of MATLAB, but many of the notebooks in the machine-learning notebook are runable.

We encounrage anyone interesting in the project to start by installing the Docker image located in the rumble-classifier and then running the notebook located in the image-processing folder.

Other areas of interst are the devops-pipeline area where we were able to test the plausibility of deploying ML on the Edge, as well as the `machine-learning' folder that holds much of the research and thinking behind the rumble classifer app.

Footnotes

1. Elephant statistics related to populations in decline, "https://www.savetheelephants.org/about-elephants-2-3-2/statistics/" ↩

2. CITEs ban on elephant ivory trade, "https://cites.org/eng/news/Current_rules_commercial_international_trade_elephant_ivory_under_CITES_Proposals_CITES_CoP17_200716" ↩

3. Who really buys elephant ivory? "https://www.nationalgeographic.com/pages/article/150812-elephant-ivory-demand-wildlife-trafficking-china-world" ↩

4. What are the greatest threats to elephants? "https://www.elephantvoices.org/support-now/7-elephantvoices/support-now/52-main-threats-to-elephants.html" ↩

5. Monitoring Elephant Populations and Assessing Threats a manual for researchers, managers, and conservationists by Simon Hedges (Ed.) https://www.fws.gov/international/pdf/Monitoring_Elephant_Populations_and_Assessing_Threats_to_press_version.pdf ↩

6. Elephant MSR (Mirror Self-recognition) https://www.pnas.org/content/103/45/17053?utm_source=TrendMD&utm_medium=cpc&utm_campaign=Proc_Natl_Acad_Sci_U_S_A_TrendMD_0 ↩

7. Elephant social skills and behavior "https://www.pnas.org/content/108/12/5116.short" ↩

8. Katy Payne "https://www.nytimes.com/1993/11/09/science/scientist-at-work-katy-payne-picking-up-mammals-deep-notes.html" ↩

9. Complex vibratory patterns in an elephant larynx (https://jeb.biologists.org/content/216/21/4054) ↩

10. Elephant communication (https://www.elephantvoices.org/elephant-communication/acoustic-communication.html) ↩

11. Do elephants make alarm call that means, "humans!" (https://earthsky.org/earth/do-elephants-make-alarm-call-that-means-humans) ↩