I have conducted an MSc thesis in data-science applied on bioacoustics data, and wish to carry on some experiments on my own now, using domestic environment as a lab.
I am looking for devices to record the vocalizations, the position and the movement of the animal.
I am thinking about microphones, GIS trackers and accelerometers. The best is they are small and light enough to be carried without problems by a dog, without bothering or altering the behaviour.
Preferences:
- Should be installed on collars or be carried by the animal without affecting its behaviour
- water-proof or water-resistant (to rain or < 1m pressure would be enough)
1. Microphones
Anyone could suggest brands and types of affordable and market-available of micro-phone recorders for pets (dogs) ?
Dogs acoustic perception is affine to human audible spectrum.
Requirements:
- Recording Sampling rate 67-45,000 Hz (better if 88,000 Hz)
- Rechargeable batteries
2. GIS trackers
Which market-available sensors would you suggest, adapted for the scope?
It is ok low resolution, I don't know which is correct comprosime. Ideally 1m would be enough, but don't know about GIS sensors. Can you share some info and aspects I should take into account ?
3. Accelerometers
Ideally they should be very light so that I can install them or collar or adjust with other non-invasive scaffolding, if the sensor is sold off-the-shelf with no case.
Requirements:
- Must record 3D axes
Which recording rate would you suggest for similar projects?
Thanks for sharing tips and info !
11 September 2023 4:33pm
Hi Luigi!
You should have a look at the μMoth
developed by @alex_rogers and others from Open Acoustics Devices:
As an alternative audiologger meant to be animal borne, check out the Audiologger developed by Simon Chamaillé-Jammes @schamaille et al :
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
This one can also log acceleration and magnetometry! We have recently deployed it on muskoxen in Greenland.
For a GPS tracker, you may want take a look at the SnapperGPS by @JonasBchrt & @alex_rogers :
As an alternative the i-gotU GPS logger may be of interest:
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
Regarding your question on sampling frq: We have been using 8Hz (and 10 Hz on the Audiologger Acceleration logging) for our slow moving muskoxen. For an animal like a dog, you probably want to sample at somewhat higher frq. This group used 50Hz in a study of arctic fox:
17 December 2023 3:02pm
I am not an acoustics person but train and deploy canines in the field. Are you looking for something that records sniff rate and patterns? For GPS I just use a Garmin collar system Altha 100. There is a Conservation Canine group that might be worth asking your question in.
Lars Holst Hansen
Aarhus University