How is it possible plotting frequency vs. time in audio?
For traditional signal processing, time and frequency are dual variables, linked through Fourier transformations. This strong linkage yields a balance, sometimes called Heisenberg–Pauli–Weyl uncertainty inequalities. In everyday words, one could not be arbitrarily precise in time location and frequency determination. One cannot determine the frequency of a single sample.

However, in audio, meaningful information is usually carried by chunks of time, or collections of samples. So very often, people perform Fourier-like transformations over those consecutive or overlapping time chunks. They obtain information on how frequency contents evolve over time. A lot of specific methods, termed time-frequency analysis, have thus been developed, to provide  us with useful algorithmic tools to perform such analyses. They are legion: oversampled filter bank, spectrogram, Wigner-Ville, Choi-Williams, reassigned versions, S-transform, wavelets, etc. 

In the tutorial: Time-Frequency Toolbox For Use with MATLAB (150 pages, no less), there are many comparisons. On a piecewise regular signal (sine then tone and linear chirp), they provide the following comparative plots that you can reproduce for your purpose:







One uses them  in audio for several reasons: to  analyze stationary parts, detect the onset of certains frequencies or harmonics, detect hearable information. Such techniques are at the very heart of mp3 audio compression for instance.