Empowering Acoustic Research

Advancing Acoustical Research on Unrecorded native and invasive species:

 

Our Organization seeks to conduct pioneering acoustical research aimed at identifying and understanding previously undocumented mammalian, avian, reptilian, insect species through sound analysis. By leveraging advanced technology and innovative methodologies, our interdisciplinary team aims to unlock new insights into the biodiversity of mammalian, avian, reptilian, insect species globally, contributing to conservation efforts and expanding our understanding of animal communication and behavior.

 

Many mammalian, avian, reptilian, insect species remain unidentified or poorly understood due to various factors, including remote habitats, nocturnal behavior, and cryptic nature, changing weather patterns and human activity. Traditional methods of identification often prove challenging or ineffective. Acoustic analysis offers a promising approach to bridge this gap, as it provides a non-invasive means to identify species based on their unique vocalizations and calls.

 

With conservation as one of our main objectives we collect no samples and take no species. We just record the sounds they make and atmospheric conditions while they're doing it!! 

 

Project Objectives and Our non-invasive approach:

 

Deploy advanced recording equipment: We will utilize state-of-the-art recording devices equipped with high-fidelity microphones and data logging capabilities to capture acoustical signals in diverse habitats.

 

Develop machine learning algorithms: Our team will develop custom machine learning algorithms trained on known mammalian, avian, reptilian, insect vocalizations to automatically detect and classify unidentified species' calls from large datasets.

 

Field expeditions and data collection: Fieldwork expeditions will be conducted to collect audio recordings from various habitats, including forests, grasslands, and wetlands, where unidentified mammalian, avian, reptilian, insect species are suspected to reside.

 

Collaborative research: We will collaborate with local researchers, conservation organizations, and indigenous communities to access remote areas, share knowledge, and ensure the ethical and sustainable conduct of our research.

 

Data analysis and dissemination: Acoustic data will be analyzed using a combination of manual and automated methods, with findings disseminated through scientific publications, conferences, and educational outreach programs.

 

Method for collecting audio samples:

Sampling Sites: Fieldwork expeditions will be conducted to collect audio recordings from various habitats, including forests, grasslands, and wetlands, where unidentified mammalian, avian, reptilian, insect species are suspected to reside.

 

With particular interest in solid granite topography near water for the use of the highly reflective surfaces. 

 

Equipment Setup: Use high-quality, omnidirectional microphones placed at consistent heights above ground to minimize bias. 

 

Sampling Protocol: Establish a standardized sampling protocol for our data set using (.wav format) maximizing recording quality, sampling frequency from 0.05hz to 25000Khz, with the ability to pick up low frequency rumbling, we should also get some interesting seismic recordings. The USGS and NASA use a similar infrasound microphone to collect this data.

 

Data Collection: Conduct audio recordings using digital recorders synchronized with GPS devices to accurately document sampling locations. Record ambient noise levels continuously to account for variations and label items for discard like; loud vehicles, Air traffic passing overhead, thunderstorms, wind, random peoples conversations.

 

Data Management: files will be organized systematically, labeling them with date, time, and location information. Using an onboard mini weather station and data logger to record environmental conditions such as temperature, humidity, and other atmospheric conditions with the Pascal rating being one of The most important.

 

Quality Control: Regularly monitor recordings for technical issues like wind interference or equipment malfunction. Exclude recordings with significant disturbances from analysis as expressed in data collection. The system will be able to remotely notify us of any no-work condition such as battery fault, GPS position loss, and a multitude of other failures via iot.

 

Analysis: Analyze audio recordings using spectrogram analysis software to identify and classify sound sources, such as bird calls, insect chirps, or mammal vocalizations. Quantify sound characteristics such as frequency, duration, and amplitude.

 

Acoustic data will be analyzed using a combination of manual (human ears) and automated methods (signal analysis and machine learning algorithm)

 

Our team is developing a machine learning algorithm: A custom machine learning algorithm trained on known mammalian, avian, reptilian, insect vocalizations to automatically detect and classify unidentified species' calls from large datasets.

 

The algorithm will be able to flag anomalous activity for human listening and confirmation.

 

The algorithm would be able to listen to thousands of hours of audio and classify it in just a few days rather than a human being listening to three microphones over a one-month project would need close to 500 hours to do the same thing. That's 21 days of audio. A human being would have to listen to, With just three microphones deployed for one week.

Findings: will be disseminated through scientific publications, conferences, and educational outreach programs and available free to any organization including that of the US department of agriculture.

 

Interpretation: We look to make no interpretation on these findings. We look only to create a very large data set with a very large catalog of sounds of wildlife globally. 

 

(Think about when tigers are gone. We hope that these recordings will someday help generations of future earthlings hear very good recordings of the sounds of animals that might become extinct and very soon with the proposed climate change effects.)

 

Documentation and Reporting: All of our spectrograph data and all of our weather data will be available publicly.

 

By following this method, we believe we can systematically collect and analyze audio from thousands of species of wildlife with the hopes of discovering new sounds no one has ever heard or captured before. We hope to gain valuable insights into forest ecosystems.

 

The importance of recording the weather during this survey: 

 

Weather Station Setup: a weather station attached to each sampling site to continuously monitor meteorological parameters such as temperature, humidity, precipitation, wind speed, and barometric pressure.

 

Data Synchronization: GPS-synchronized clocks and data logging systems to maintain temporal consistency.

 

Continuous Monitoring: The weather station collects data at regular intervals (e.g., every 15 minutes) throughout the sampling period. This provides a comprehensive record of weather conditions during audio recording sessions.

 

Data Integration: Merge weather data with audio recordings using timestamps to create a unified dataset for analysis. This allows for correlation analysis between weather variables and sound patterns.

 

Analysis: Use statistical methods to examine relationships between weather variables and

acoustic parameters such as sound intensity, frequency modulation, and species richness. For example, you can assess how changes in temperature or humidity influence bird vocalizations or insect activity. Also if the The vocalization has been changed from its original characteristics i.e. changed in pitch or tone.

 

Visualization: Create visualizations such as scatter plots, time series graphs, or heatmaps to illustrate the relationships between weather conditions and acoustic features. This facilitates the interpretation of complex data patterns.

 

Interpretation: Interpret the results in the context of ecological processes and species interactions. For instance, warmer temperatures may increase amphibian calling activity, while wind speed may affect the propagation of sound through the forest canopy. Climate change could be affecting the local wildlife population in a way previously unidentified. This research may help provide a link with these data sets provided they are available publicly to everybody.

 

Documentation: Weather monitoring protocols, data collection form on board Device with Data logger with analysis procedures in detail to ensure transparency and reproducibility of the study.

 

By following this method, we hope to systematically collect and analyze audio samples to gain valuable insights into forest ecosystems.

 

The Device:

Stand alone CZM (Compression Zone Mic) remote deployment platform prototype.

Our prototype is capable of consistently capturing top-quality audio for 7 to 12 days while operating solely on battery power. With the expectation that it will be able to operate longer utilizing solar recharging capabilities as well as data uplink via iot.

 

This device facilitates long-term deployment as a high-fidelity wildlife recording station managed by The A.R.C. team, equipped with satellite connectivity for regular status updates via iot.

Bi-Weekly physical In person checks of the device will be needed to remove full storage cards and replace them with empty ones.

 

Alternative configuration and abilities: 

Other configuration options and capabilities include deploying three of these microphones within their range to triangulate the precise location of specific wildlife, aiding specialized researchers in narrowing down search areas.

 

Additionally, we aspire to produce multiple units suitable for deployment across the United States and with big hope the globe! Deployed by researchers with limited technical expertise, particularly in regions of high wildlife activity, to compile a comprehensive database of calls and other sounds of any known wildlife with the hope of identification of presently undiscovered species. Or at the very least capturing the sounds they make.