"Sparsification" of Audio Signals Using the MDCT/IntMDCT and a Psychoacoustic Model—Application to Informed Audio Source Separation
Sparse representations have proved a very useful tool in a variety of domain, e.g. speech/music source separation. As strictly sparse representations (in the sense of l0) are often impossible to achieve, other ways of studying signals sparsity have been proposed. In this paper, we revisit the irrelevance filtering analysis-synthesis approach proposed in (Balazs et al., IEEE Trans. ASLP, 18(1), 2010), where the TF coefficients that are below some masking threshold are set to zero. Instead of using the Gabor transform and a specific psychoacoustic model, we use tools directly inspired from perceptual audio coding, for instance MPEG-AAC. We show that significantly better "sparsification performances" are obtained on music signals, at lower computational cost. We then apply the sparsification process to the informed source separation (ISS) problem and show that it enables to significantly decrease the computational cost at the ISS decoder.
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members and is temporarily free for AES members.