What bird is that?

Abstract: The presence of birds in an ecosystem is often a good indicator of the overall biodiversity. Since birds can be hard to see, their sounds are often used instead to measure their presence. To automatically detect birds the most common method is to use a time-frequency representation together with a convolutional neural network. The most used time-frequency representation is called the spectrogram. An alternative to this is the Wigner-Ville distribution (WVD). The purpose of this thesis is to investigate if bird classification can be improved if the WVD is used instead of the spectrogram. The bird sounds were gathered from the website xeno-canto.org. Nine bird species were selected and there were in total 859 samples of bird songs. To achieve the purpose, four different methods were used. The first one compared the spectrogram to the WVD. The second one compared the spectrogram to the smoothed pseudo Wigner-Ville distribution (SPWVD). The third one compared the spectrogram to several SPWVD's, performed on shorter sound segments. The last one investigated if a high pass filter could improve the methods. The WVD and its variations performed worse than the spectrogram for all methods. The best result for the spectrogram was 79\% while the best result for the WVD came from a variant of the SPWVD. Its maximum accuracy was 70\%. The poor performance of the WVD is likely, in part, a result of the high computational requirements for the WVD. As a result of this, much shorter sound segments could be utilised for the WVD compared to the spectrogram. In the future it is likely that the computer power available will far exceed the current availability thus giving the WVD a better chance.