After some long admin time and other projects in the way, we are really happy to share the next steps in our project, Shazam for Bats - GOSH ColDev. We were awarded the “New Project Track” funding from GOSH’s 2022 Collaborative Development Program - Round 1 for this project a few months ago.
We will be using this thread to post updates from the project. In this post, we are sharing the next steps for the projects and the things we need to tackle in the next 3 months. We would love your participation and hope you will be interested in following our steps.
Our aims for the next phase of this project are:
- To classify bats’ calls on a Raspberry Pi.
- To optimise for low-power consumption and long-range transmission.
- To document the project: the enclosure, the assembly, the installation, and the deployment.
Migration to RPi
Migrate Bat Classifier Algorithm to RPi
Rework data transmission
Rework database storage
Redesign & Rework Enclosure
3D Printing Section
Document HW & SW
HW Schematic, Assembly Manual
SW Installation Manual
The advancement in the project will happen mainly on the GitHub Repository Shazam4BB. The repo is currently being built, but you can expect the following by the end of the project:
- Documentation Folder: (1) design files to build enclosure, (2) manual for assembly, (3) manual for installation, (4) manual for operating the device.
- Scripts Folder: (1) script to record, process, and analyse audio files, (2) script to transfer data and access data.
If you are interested in the current “Shazam for Bat” device, you have a look at this blogpost.
We are looking forward to keeping you posted on our progress and look forward to hearing your ideas, comments, and remarks.
Please feel free to directly comment on this post, write to @audevuilli or drop us an issue in the GitHub repository.
Thank you to GOSH and the GOSH Community for all your help and support.
Project Dates Summary
- Oct 22 - Award agreement signed.
- Dec 22 - Funds became available.
- Jan 23 - Project deadline extended to April 23.
Looking forward to the full BOM and some initial instructions. I have some bats living in the waterwheel here who I would love to identify.
So I am very keen to help beta-test your instructions.
We would like to update you on the Shazam for Bats project.
- Feb-Apr 23 - Develop Software Application to run BatDetect2 deep-learning model on RaspberryPi 4B.
- Apr 23 - Deploy and test application over a week.
- Apr 23 - Make BOM and purchase items to test 3 long-term deployments in the wild.
- Mai 23 - Present version 1 of the system at the BritBats23 Conference.
- After discussing how to develop a bat classifier for single-board computers, we decided to take a step back and not only develop an application for bats but an application that could easily be modified to be used in other acoustic classification tasks.
- We set up the following design requirement to sketch and develop such an application: (1) recording parameters such as recording duration and intervals are easily modifiable, (2) different types of ML acoustic models can be implemented, (3) options are available for saving and deleting recordings and detections files.
Below is the process diagram that summarises the first version of the application that implements the BatDetect2 algorithm developed by Oisin Mac Aodha and Santiago Martinez to detect bat calls and classify bat species.
- The application has been deployed and tested for a week. The results are promising. The next step is to deploy 3 devices in the Queen Elizabeth Olympic Park (QEOP) in London with an updated version of the application. The updated version sends the bat calls detection and classification directly to a remote server via MQTT.
- Finalise documentation for the software application.
- Finalise documentation for deploying devices.
- Put together the 3 devices to be deployed.
- Friday 5th May, the BritBats Conference is taking place in Bristol. We will discuss the BatDetect2 model and present the first version of Shazam4Bats.
- We are currently developing the application in a closed GitHub repository but are looking to open it to the public once we have finalised the documentation and run a few more tests. Hopefully, in the next few weeks We will keep you posted on the release.
- We will soon be looking for people to test the implementation. So keep an eye open!
What kinds of community contributions would be helpful for your project, @audevuilli?
I have a couple RaspberryPi 4Bs that I could help test with as you get further along. Regardless, I’m very excited to see your work progress!