Take a step back and look into the future

Hello friends, this is Yifan again. As the end of the summer draws near, my summer research is also coming to a conclusion. The work I did over the summer was very different from what I expected. Since this is a wrap up post for an ongoing project, let us first go through what exactly I did this summer.

The above is the product flow graph for our MDP project. All the blue blocks and paths are what I worked on this summer. In previous posts I wrote about progress and accomplishments on everything except the bird detection algorithm.

In my second blog post, I talked about using a single HMM (hidden Markov model) to differentiate between a bird and a non-bird. One problem was that HMM classification takes a long time. Running HMM classification on a 30-minute long recording takes about 2 minute. Considering the fact that we need to analyze data much longer than that, we need to pre-process the recording, and ditch the less interesting parts. This way, we are only putting the interesting parts of the recording into the HMM classifier.

This figure is the runtime profile of running HMM on a full 30-minute long recording. The classification took about 2 minutes. After splitting out the interesting parts of the recording, we are only running classification on these short clips, hence reduces the runtime by a very large factor (see figure below).

One thing you might have noticed in these two graphs is that the runtime for wav_parse is also extremely long. Since there is almost no way to get around parsing the wav file itself, the time consumed here will always be a bottleneck for our algorithm. Instead of a better parsing function, I did the mature thing by blaming it all on python’s inherent performance issues. Jokes aside, I think eventually someone will need to deal with this problem, but I think optimization can wait for now.

This figure is the raw classification output using a model trained by 5 samples of a matching bird call. If the model thinks a window in the recording matches the model, it marks that window as 0, otherwise 1. Basically this mess tells us that in these 23 clips, only clip 9 and 10 does not contain the bird used to train the model.

One might ask, why don’t you have a plot or graph for this result? Don’t yell at me yet, I have my reasons… I literally have more than a hundred clips from one 30-minute recording. It’s easier for me to quickly go through the result if they are clustered together in a text file.

Although me and my mentor Stanislav had decided on trying out HMM to do the bird detection. The results aren’t very optimistic. There is the possibility that HMM is not a very good choice for this purpose after all, which means I might need to do more research to find a better solution for bird detection. Luckily, since songbird is an ongoing project, I will get my full team back again in September. Over this summer, I believed I have made some valuable contributions to this project, and hopefully that can help us achieve our initial goals and plans for this product.

This summer has been a wonderful time to me. I would like to thank all my mentors and fellows for their help along the way, it really meant a lot to me. Looking into the future, I definitely believe this project has more potential than just classifying birds, but for now I am ready to enjoy the rest of the summer in order to work harder when I come back to Ann Arbor in fall.