Extracting feature from audio track using DWT
Four features is a very short description for an audio signal. The hint: "Write a program to transform an audio track to time-frequency domain" is important. Since audio signals are in general not fully stationary, windowed-Fourier or Short-term Fourier transforms can be important. Such representations are generally redundant, yet one can expect that the most energetic features in those domains represent the main energy in the time domain.

One first bet is to use a time-frequency spectrogram, and pick the most important features, or the highest local maxima. 

Then, if needed, you can think about reducing the redundancy with filter-banks or discrete wavelet transforms. You can further  incorporate auditory system weighting, or classification priors.