How to represent audio data in a format that can be used for preprocessing and modelling?
Audio .wav codec file has a 44 byte header which will give you critical data like bit depth ( CD quality audio is 16 bits per sample), sample rate ( CD quality uses 44,100 audio samples per second ), number of channels, etc ... the balance of bytes in a .wav file is the payload which is the audio curve stored as a set of integers which define the height of the audio curve across a succession of instances in time. The beauty of .wav is its not compressed so all bytes after the header only store the audio curve.

Many audio libraries will allow you read a wav file and return back a floating point array of samples per channel.  Visualize a dog bark, this sound propagates from the dog and lands on the membrane of a microphone.  Similar wobbling different mediums.  This wobbling recorded over time can be represented as a time series curve ... as its digitized its transformed from analog to digital ( ADC ) the original analog audio wave is ~sampled~ X number of times per second to generate a simplification of that analog curve during this analog to digital conversion.  Each audio sample represents the height of that audio curve at a specific instant in time.  

If you only devote 1 bit to record the audio curve, for a given sample, this will transition from 0 to 1 as the audio curve wobbles above and below the stationary silence flat line mark.  Whereas a bit depth of 16 bits which can store 2^16 distinct integers allows far greater granularity and so more accurately models the height of the audio curve.

Unlike image processing where conceptually the entire dataset can be ~consumed~ in an instant of time, audio has an inherent dimension of time.  Audio is a time series.  Another attribute of audio is the simplicity with which any arbitrary audio curve can be represented by a set of pure sin curves, each with an amplitude, frequency and phase shift.  Joseph Fourier outlined the theory whereby time domain data like audio can be represented equally well in either the time domain ( wav codec or analog audio curve ) or in the frequency domain.  It does not go unnoticed that this symmetry implies a conservation of information.

To an arbitrary degree of precision a window of audio samples ( in the time domain ) can be transformed using a Fourier Transform into its frequency domain representation with a controllable degree of information loss or lack thereof.  Be aware that as you increase the number of samples in this window you decrease the temporal specificity of the resultant spectrogram ... meaning the ability to pin point when a frequency occurred is aided by using the lowest number of audio samples in your window send into the FFT call ... although the frequency resolution in the freq domain is increased by feeding in a larger number of samples to your FFT call ... there is no such thing as a free lunch

So now you have options ... perform your analysis of audio while in its native time domain or send it into a FFT call and have the same information as a set of frequency bins where each bin has values for ( frequency , amplitude, phase shift )

This should give you ideas to tease apart and dig into - take care