How to preprocess audio data for input into a Neural Network
I would split each wav by the areas of silence. Trim the silence from beginning and end. Then I'd run each one through a FFT for different sections. Smaller ones at the beginning of the sound. Then I'd normalise the frequencies against the fundamental. Then I'd feed the results into the NN as a 3d array of volumes, frequencies and times.
I'm currently developing a keyword-spotting system that recognizes digits from 0 to 9 using deep neural networks. I have a dataset of people saying the numbers(namely the TIDIGITS dataset, collected at Texas Instruments, Inc), however the data is not prepared to be fed into a neural network, because not all the audio data have the same audio length, plus some of the files contain several digits being spoken in sequence, like "one two three".
Can anyone tell me how would I transform these wav files into 1 second wav files containing only the sound of one digit? Is there any way to automatically do this? Preparing the audio files individually would be time expensive.
Thank you in advance!