I was looking into the possibility to classify sound (for example sounds of animals) using spectrograms. The idea is to use a deep convolutional neural networks to recognize segments in the spectrogram and output one (or many) class labels. This is not a new idea (see for example whale sound classification or music style recognition).
The problem that I'm facing is that I have sound files of different length and therefore spectrograms of different sizes. So far, every approach I have seen uses a fixed size sound sample but I can't do that because my sound file might be 10 seconds or 2 minutes long.
With, for example, a bird sound in the beginning and a frog sound at the end (output should be "Bird, Frog"). My current solution would be to add a temporal component to the neural network (creating more of a recurrent neural network) but I would like to keep it simple for now. Any ideas, links, tutorials, ...?