"Sparsification" of Audio Signals Using the MDCT/IntMDCT and a Psychoacoustic Model—Application to Informed Audio Source Separation

Pinel J, Girin L. "Sparsification" of Audio Signals Using the MDCT/IntMDCT and a Psychoacoustic Model—Application to Informed Audio Source Separation. In: AES Conference: 42nd International Conference: Semantic Audio. Audio Engineering Society; 2011. Paper 4-4. Available from: https://aes.org/publications/elibrary-page/?id=15956

@inproceedings{Pinel2011_15956,
  author = {Pinel, Jonathan and Girin, Laurent},
  title = {{"Sparsification" of Audio Signals Using the MDCT/IntMDCT and a Psychoacoustic Model—Application to Informed Audio Source Separation}},
  booktitle = {AES Conference: 42nd International Conference: Semantic Audio},
  note = {Paper 4-4},
  year = {2011},
  month = jul,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=15956}
}

TY  - CPAPER
TI  - "Sparsification" of Audio Signals Using the MDCT/IntMDCT and a Psychoacoustic Model—Application to Informed Audio Source Separation
AU  - Pinel, Jonathan
AU  - Girin, Laurent
T2  - AES Conference: 42nd International Conference: Semantic Audio
M1  - Paper 4-4
PY  - 2011
DA  - 2011/07/06
UR  - https://aes.org/publications/elibrary-page/?id=15956
PB  - Audio Engineering Society
LA  - en
AB  - 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.
ER  -