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In this paper, it is proposed to apply the discrete cosine transform (DCT) and the compressive sampling (CS) techniques to audio signal compression. Using spectral analysis and the properties of the DCT, audio signals can be treated as sparse signals in the frequency domain. This is especially true for sounds representing tones. Thus, it is proposed using the DCT as a preprocessor to obtain a sparse representation in the frequency domain, combined with CS to obtain an efficient representation of the signals. It is shown that the subsequent application of CS represents the signals with less information than the well-known sampling theorem. [ABSTRACT FROM AUTHOR]

To increase the efficiency of the independent component analysis (ICA) and reduce the computational complexity of the system, this paper proposes a methodology based on the compressive sampling with minimum measurements. This allows compressing and modifying the Gaussian characteristics of audio signals. ICA is one of the most widely used schemes for separating the sources involved in an audio mixture received in a set of sensors. However, for proper operation it is required that the signals involved in the mixture do not have Gaussian characteristics or at most only one of them be Gaussian, characteristics that are not satisfied by the audio signals, which have in general Gaussian characteristics. The proposed methodology allows obtaining a better separation with lower computational complexity, and with a more efficient separation of the mixed signals, even if the signals involved in the mixture are of Gaussian toe. [ABSTRACT FROM AUTHOR]