I’m posting here to ask for a some advice. Sorry in advance for the long post.
I’m currently studying for an integrated masters in Electrical and Information Engineering at the University of Cambridge. I’ve been lucky that my supervisor has been very open with collaborating with me to design my final year masters project. I’m really interested in conservation and biodiversity monitoring and would love to design a project around this. Unfortunately, though, the engineering department hasn’t ever done a project in this area! The project doesn’t have to break the internet from my point of view, personally I’d like to spend my year working in this field and learning something really useful, rather than researching hardcore engineering.
My supervisor is very interdisciplinary and knowledgeable in product development and we are both trying to learn the current issues in conservation. I have been reading papers and trying to get a lay of the land. We are thinking that acoustic monitoring seems like an interesting area that could provide a project that is attainable given the time frame of the Master's project as well as "technical" enough to be considered an engineering Master’s. My experience is in analogue and digital electronics, some processor coding and then more experience in signal processing, inference and ML.
I’m trying to contact knowledgable people outside the engineering faculty to help with generating ideas / bring some expertise to the process. Currently we are considering ideas of:
1. Localisation of sound (in 2D or 3D).
I know there is some work already in this area. The list of bioacoustics software recently posted in this group has some links to them. I am wondering if there is scope to improve these. For example some use DOA analytical models - is there a way to achieve localisation through ML methods? Or through networked sensors? Or by localising in real-time so that a camera could be moved to take a photo of the noise?
I love the idea of the AudioMoth and the low-cost, lightweight, low power applications that drive it's design. I found their original paper great and the most recent paper about low-power detection algorithms fascinating. There are a lot of papers out there throwing around extremely complex variations on neural nets and I really liked how the AudioMoth team targeted the design of these algorithms to the problem!
I like the design parameters of the problem the AudioMoth is trying to solve. What I have been struggling with is then coming up with a suitable project that involves the AudioMoth. One idea was to integrate (from a paper that came out earlier this year) an analogue, programmable pattern recognition circuit with micro-watt power dissipation. Or attempting to expand upon the low-power detection algorithms.
The project runs for 16 weeks of term time, with 6 weeks of vacation in the middle to work on it as well. The final few weeks are generally reserved for exam study and project write up.
I realise this is quite a large dump of thoughts and I would be so grateful for any help, resources that you could point me towards or thoughts on our current ideas.
All the best,