AudioMoth | Open Acoustic Devices

openacousticdevices

Energy-Efficient Audio Processing at the Edge for Biologging Applications

Biologging refers to the use of animal-borne recording devices to study wildlife behavior. In the case of audio recording, such devices generate large amounts of data over several months, and thus require some level of processing automation for the raw data collected. Academics have widely adopted offline deep-learning-classification algorithms to extract meaningful information from large datasets, mainly using time-frequency signal representations such as spectrograms. Because of the high deployment costs of animal-borne devices, the autonomy/weight ratio remains by far the fundamental concern. Basically, power consumption is addressed using onboard mass storage (no wireless transmission), yet the energy cost associated with data storage activity is far from negligible. In this paper, we evaluate various strategies to reduce the amount of stored data, making the fair assumption that audio will be categorized using a deep-learning classifier at some point of the process. This assumption opens up several scenarios, from straightforward raw audio storage paired with further offline classification on one side, to a fully embedded AI engine on the other side, with embedded audio compression or feature extraction in between. This paper investigates three approaches focusing on data-dimension reduction: (i) traditional inline audio compression, namely ADPCM and MP3, (ii) full deep-learning classification at the edge, and (iii) embedded pre-processing that only computes and stores spectrograms for later offline classification. We characterized each approach in terms of total (sensor + CPU + mass-storage) edge power consumption (i.e., recorder autonomy) and classification accuracy. Our results demonstrate that ADPCM encoding brings 17.6% energy savings compared to the baseline system (i.e., uncompressed raw audio samples). Using such compressed data, a state-of-the-art spectrogram-based classification model still achieves 91.25% accuracy on open speech datasets. Performing inline data-preparation can significantly reduce the amount of stored data allowing for a 19.8% energy saving compared to the baseline system, while still achieving 89% accuracy during classification. These results show that while massive data reduction can be achieved through the use of inline computation of spectrograms, it translates to little benefit on device autonomy when compared to ADPCM encoding, with the added downside of losing original audio information.

MDPI

SnapperGPS - Home

Home page of SnapperGPS - A small, low-cost, low-power wildlife tracking system.

i-gotU GT-120B GPS / GNSS Data Logger - Water Resistant, 21g only, Managing Large Deployments with Ease (2022 Edition)

(USB / Wireless dual interfaces, GPS and QZSS multiple constellations, Windows, Android and IOS compatible) Compared to previous models (i.e. GT 120) which are GPS, GT-120B is a GNSS logger that utilizes both GPS and QZSS constellations. It has multi-path detection, which dramatically eliminates Ionospheric error and multi-path effects. Compared to previous GPS models, the data accuracy is significantly better.  GT-120B has usb and wireless dual interfaces, which allows data  to be  downloaded either via either usb or wirelessly. Rather than using the proprietary USB cable for GT-120, GT-120B uses a standard micros-usb cable.  GT-120B can be used as an usb GNSS receiver with 1-10Hz update rates. When used as a GNSS data logger, the update rate is 1 Hz. Managing large deployments of GT-120B with ease The I-gotU GT-120B comes with mobile and Windows apps which help manage a large number of loggers.  1. You can view all your GT-120B devices on Google maps from your mobile phone app. 2. You can self define a group, add loggers to the group and select tracks from the group. 3. From your mobile phone, you can keep track of battery and memory statuses of all your GT-120B devices. 4. Not only can you backup your device settings, you can also standardize the settings of a bunch of devices by using import / export features. 5. If you want to protect the GT-120B data from unauthorized downloads, you can enable password check settings. 6. GT-120B can be turned on / off by a predefined schedule. Battery runtime by GPS log interval GPS log interval Battery runtime 1 sec 5 sec 15hr 10 sec 25hr 15 sec 60hr 30 sec 120hr 60 sec 180hr 60 min 2 months Logging configuration options Configurable GPS Logging interval 1sec~60min59sec Circular Logging YES POI YES Scheduled Logging YES Merge scheduled waypoints YES Smart Tracking YES Power triggered auto-logging YES    Technical Spec: Dimension 44.5x28.5x13.8mm Weight 21.5g Wireless connect with mobile phones Yes  Wireless Chipset Nordic nRF 52820 Wireless range 20m GPS Chipset MTK MT3337 Antenna Patch Antenna Channels   22 tracking / 66 acquisition-channel GPS receiver; Supports up to 210 PRN channels; GNSS support GPS & QZSS SBAS support WAAS/EGNOS/MSAS/GAGAN Other enhancement     12 multi-tone active interference cancellers (with ISSCC2011 award); Indoor and outdoor multi-path detection and compensation; Internal real-time clock (RTC); RTCM ready YES NEMA support NMEA 0183 standard V3.01 and backward compliance. Supports 219 different data update rates for position 10 Hz GPS Sensitivity Acquisition: -148 dBm (cold) / -163 dBm(hot) Tracking: -165 dBm Cold Start < 35sec Warm Start < 34sec Hot Start <1 sec USB cable micro-usb, USB 2.0 Battery 380mAh LED Blue & Red Operating Temperature -10 ~ +50°C Water-resist YES GPS Logger YES GPS Receiver USB Memory 65000 Motion Detection NO Disable Button YES Disable Wireless YES Disable LED flashing YES Setup wireless download password YES Enable wireless upon schedule YES Configure wireless broadcast interval YES Broadcast latest GPS position YES Configure wireless TX Power (output power of wireless signal) YES Rename device (such as nickname) YES Power Saving Option above 7sec NO Firmware update via PC software Device configuration via USB or wirelessly Data download via USB or wirelessly Combined maps YES GPS Data Import format GPX GPS Data Export format GPX, CSV  Software and compatibility: GT-120B comes with below software: Windows App: compatible with Windows 7, 8, 10 & 10 IOS App: compatible with IOS 12 and above Android App: compatible with Android 7 and above “I-gotU GPS” IOS / Android apps: GT-120B can connect to the “I-gotU GPS” app on iphone/Android wirelessly. The “I-gotU GPS” app has the below features: Wireless configurationInstead of connecting through a USB cable, you can now wirelessly change configuration settings of GT-120B through the iphone/Android app. Wireless data downloadYou can wirelessly download the log data from GT-120B to your smartphone. Battery and memory status on AppTo check the status of the device’s battery and memory, open the "i-gotU GPS" app. Find my Device on Google MapYou can view the locations of your devices on Google maps from your mobile phone.    “I-gotU GPS” Windows application: The new “I-gotU GPS” Windows software has the below new features: playback group movement measure distance from waypoint A to B. measure distance from different anchor points. Package content: 1 x GPS logger; 1 x USB cable; (Does NOT comes with Jelly case or fastening strap.)   Youtube videos:   - i-gotU GT-120B / GT-600B Youtube video   Documents:   - User Guide   - side by side comparison for GT-120, GT-600, GT-120B and GT600B   Sample Data Downloads:   - Sample Data File in CSV format (original data recorded by GT-120B)  - Sample Data File in GPX format (original data recorded by GT-120B)     Blogs: - How to reset the i-gotU GT-120B / GT-600B device? - Side by side comparison for GT-120, GT-600, GT-120B and GT600B   - When charging multiple GT-120B/GT-600B devices simultaneously, please avoid the following.   Software Downloads: (To download the app for Windows, iOS or Android devices, simply click the link below that corresponds to your device.)  

CanadaGPS.ca

DIY Instructions

After the two day acclimation period, with the GPS is programed, insert the GPS unit into the case and proceed to track your cat for a 10 day period.

Cat Tracker 2.0

Digging into the behaviour of an active hunting predator: arctic fox prey caching events revealed by accelerometry - Movement Ecology

Background Biologging now allows detailed recording of animal movement, thus informing behavioural ecology in ways unthinkable just a few years ago. In particular, combining GPS and accelerometry allows spatially explicit tracking of various behaviours, including predation events in large terrestrial mammalian predators. Specifically, identification of location clusters resulting from prey handling allows efficient location of killing events. For small predators with short prey handling times, however, identifying predation events through technology remains unresolved. We propose that a promising avenue emerges when specific foraging behaviours generate diagnostic acceleration patterns. One such example is the caching behaviour of the arctic fox (Vulpes lagopus), an active hunting predator strongly relying on food storage when living in proximity to bird colonies. Methods We equipped 16 Arctic foxes from Bylot Island (Nunavut, Canada) with GPS and accelerometers, yielding 23 fox-summers of movement data. Accelerometers recorded tri-axial acceleration at 50 Hz while we obtained a sample of simultaneous video recordings of fox behaviour. Multiple supervised machine learning algorithms were tested to classify accelerometry data into 4 behaviours: motionless, running, walking and digging, the latter being associated with food caching. Finally, we assessed the spatio-temporal concordance of fox digging and greater snow goose (Anser caerulescens antlanticus) nesting, to test the ecological relevance of our behavioural classification in a well-known study system dominated by top-down trophic interactions. Results The random forest model yielded the best behavioural classification, with accuracies for each behaviour over 96%. Overall, arctic foxes spent 49% of the time motionless, 34% running, 9% walking, and 8% digging. The probability of digging increased with goose nest density and this result held during both goose egg incubation and brooding periods. Conclusions Accelerometry combined with GPS allowed us to track across space and time a critical foraging behaviour from a small active hunting predator, informing on spatio-temporal distribution of predation risk in an Arctic vertebrate community. Our study opens new possibilities for assessing the foraging behaviour of terrestrial predators, a key step to disentangle the subtle mechanisms structuring many predator–prey interactions and trophic networks.

BioMed Central