  The application of Compressive sensing approach to the speech and musical
signals is considered in this paper. Compressive sensing (CS) is a new approach
to the signal sampling that allows signal reconstruction from a small set of
randomly acquired samples. This method is developed for the signals that
exhibit the sparsity in a certain domain. Here we have observed two sparsity
domains: discrete Fourier and discrete cosine transform domain. Furthermore,
two different types of audio signals are analyzed in terms of sparsity and CS
performance - musical and speech signals. Comparative analysis of the CS
reconstruction using different number of signal samples is performed in the two
domains of sparsity. It is shown that the CS can be successfully applied to
both, musical and speech signals, but the speech signals are more demanding in
terms of the number of observations. Also, our results show that discrete
cosine transform domain allows better reconstruction using lower number of
observations, compared to the Fourier transform domain, for both types of
signals.
