deep learning for acoustic signals
You have an audio recording, and you want to know where certain classes of sounds are. SongExplorer is trained to recognize such words by manually giving it a few examples. It will then automatically calculate the probability, over time, of when those words occur in all of your recordings. Alternatively, you have two or more sets of audio recordings, and you want to know if there are differences between them. SongExplorer can automatically detect sounds in those recordings and cluster them based on how well it can distinguish between them. Applications suitable for SongExplorer include quantifying the rate or pattern of words emitted by a particular species, distinguishing a recording of one species from another, and discerning whether individuals of the same species produce different song. Underneath the hood is a deep convolutional neural network. The input is the raw audio stream, and the output is a set of mutually-exclusive probability waveforms corresponding to each word of interest.
