Data analysis

First of all it’s important to note that the data we are dealing with is not exactly big data… With only around 100 positive samples, deep neural networks are very likely to overfit on this daa. A problem we are dealing with is that it is easy to gather negative samples (just record a whole day without bats) and difficult to gather positive samples (bats are only here for about 15-20 minutes a day, and I need to manually label data). The low amount of positive samples is something we take into consideration when determining how we are going to classify the data.

Audio signal

As we can see above the amplitude of the signal is low with the noise, while the signal has high amplitudes. However, this does not mean that everything with a sound in it is a bat. At this frequency you also pick up other noises, such as rubbing your fingers together or telephone signals.

I decided to put every negative signal onto one big “negative” pile, combining telephone signals, finger-induced noise, and other stuff in one big pile.

Spectrogram

I was hoping the see the exact frequency bats produce back in our spectogram. Unfortunately it looks like my sensor picks it up as noise over ALL frequencies. Looking at the spectrogram you can still see a clear difference between bat-sound and noise. My first attempt was to use this spectrogram as input for a convolutional neural network. Unfortunately, using only a few positive samples, it was very difficult to train this network. I thus gave up on this approach.

In the end I decided to go with a “metadata approach”. I divide every second of sound in 22 parts. For each part I determine the max, min, mean, standard deviation, and max-min of the sample. The reason I take this approach is because the “bat signals” DO clearly show up as a not of high-amplitude signals in the audio visualisation. By analyzing different parts of the audio signal, I can find out if multiple parts of the signal have certain features (such as a high standard deviation), and thus detect a bat call.

