I'm happy to say I'm now supervising two PhD students, Pablo and Veronica. Veronica is working on my project all about birdsong and machine learning - so I've got some notes here about recommended reading for someone starting on this topic. It's a niche topic but it's fascinating: sound in general is fascinating, and birdsong in particular is full of many mysteries, and it's amazing to explore these mysteries through the craft of trying to get machines to understand things on our behalf.
If you're thinking of starting in this area, you need to get acquainted with: (a) birds and bird sounds; (b) sound/audio and signal processing; (c) machine learning methods. You don't need to be expert in all of those - a little naivete can go a long way!
But here are some recommended reads. I don't want to give a big exhaustive bibliography of everything that's relevant. Instead, some choice reading that I have selected because I think it satisfies all of these criteria: each paper is readable, is relevant, and is representative of a different idea/method that I think you should know. They're all journal papers, which is good because they're quite short and focused, but if you want a more complete intro I'll mention some textbooks at the end.
Wang (2003), "An industrial strength audio search algorithm"
This paper tells you how the well-known "Shazam" music recognition system works. It uses a clever idea about what is informative and invariant about a music recording. The method is not appropriate for natural sounds but it's interesting and elegant.
Bonus question: Take some time to think about why this method is not appropriate for natural sounds, and whether you could modify it so that it is.
Stowell and Plumbley (2014), "Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning"
Lots of powerful machine learning right now uses deep learning. There's lots to read on the topic. Here's a blog post that I think gives a good introduction to deep learning. Also, for this article DO read the comments! The comments contain useful discussion from some experts such as Yoshua Bengio. Then after that, this recent Nature paper is a good introduction to deep learning from some leading experts, which goes into more detail while still at the conceptual level. When you come to do practical application of deep learning, the book "Neural Networks: Tricks of the Trade" is full of good practical advice about training and experimental setup, and you'll probably get a lot out of the tutorials for the tool you use (for example I used Theano's deep learning tutorials).
Theunissen and Shaevitz (2006), "Auditory processing of vocal sounds in birds"
This one is not computer science, it's neurology - it tells you how birds recognise sounds!
A question for you: should machines listen to bird sounds in the same way that birds listen to bird sounds?
O'Grady and Pearlmutter (2006), "Convolutive non-negative matrix factorisation with a sparseness constraint"
Kershenbaum et al (2014), "Acoustic sequences in non-human animals: a tutorial review and prospectus"
Benetos et al (2013), "Automatic music transcription: challenges and future directions"
Domingos (2012), "A few useful things to know about machine learning"