A Comparison of Computational Precedence Models for Source Separation in Reverberant Environments

Hummersone C, Brookes T, Mason R. A Comparison of Computational Precedence Models for Source Separation in Reverberant Environments. In: AES Convention 128. Audio Engineering Society; 2010. Paper 7981. Available from: https://aes.org/publications/elibrary-page/?id=15278

@inproceedings{Hummersone2010_15278,
  author = {Hummersone, Christopher and Brookes, Tim and Mason, Russell},
  title = {{A Comparison of Computational Precedence Models for Source Separation in Reverberant Environments}},
  booktitle = {AES Convention 128},
  note = {Paper 7981},
  year = {2010},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=15278}
}

TY  - CPAPER
TI  - A Comparison of Computational Precedence Models for Source Separation in Reverberant Environments
AU  - Hummersone, Christopher
AU  - Brookes, Tim
AU  - Mason, Russell
T2  - AES Convention 128
M1  - Paper 7981
PY  - 2010
DA  - 2010/05/06
UR  - https://aes.org/publications/elibrary-page/?id=15278
PB  - Audio Engineering Society
LA  - en
AB  - Reverberation continues to be problematic in many areas of audio and speech processing, including source separation. The precedence effect is an important psychoacoustic tool utilised by humans to assist in localisation by suppressing reflections arising from room boundaries. Numerous computational precedence models have been developed over the years and all suggest quite different strategies for handling reverberation. However, relatively little work has been done on incorporating precedence into source separation. This paper details a study comparing several computational precedence models and their impact on the performance of a baseline separation algorithm. The models are tested in a range of reverberant rooms and with a range of other mixture parameters. Large differences in the performance of the models are observed. The results show that a model based on interaural coherence produces the greatest performance gain over the baseline algorithm.
ER  -