Evolutionary Optimization for Hearing Aids of Computational Auditory Scene Analysis

Schlesinger A, Boone MM. Evolutionary Optimization for Hearing Aids of Computational Auditory Scene Analysis. In: AES Convention 126. Audio Engineering Society; 2009. Paper 7737. Available from: https://aes.org/publications/elibrary-page/?id=14933

@inproceedings{Schlesinger2009_14933,
  author = {Schlesinger, Anton and Boone, Marinus M.},
  title = {{Evolutionary Optimization for Hearing Aids of Computational Auditory Scene Analysis}},
  booktitle = {AES Convention 126},
  note = {Paper 7737},
  year = {2009},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=14933}
}

TY  - CPAPER
TI  - Evolutionary Optimization for Hearing Aids of Computational Auditory Scene Analysis
AU  - Schlesinger, Anton
AU  - Boone, Marinus M.
T2  - AES Convention 126
M1  - Paper 7737
PY  - 2009
DA  - 2009/05/06
UR  - https://aes.org/publications/elibrary-page/?id=14933
PB  - Audio Engineering Society
LA  - en
AB  - Computational auditory scene analysis (CASA) provides an excellent means to improve speech intelligibility in adverse acoustical situations. In order to utilize algorithms of CASA in hearing aids, sets of algorithmic parameters need to be adjusted to the individual auditory performance of the listener and the acoustic scene in which they are employed. Performed manually, the optimization is an expensive procedure. We therefore developed a framework in which algorithms of CASA are automatically optimized by the principles of evolution, i.e., by a genetic algorithm. By using the speech transmission index (STI) as an objective function, the presented framework presents a holistic routine which is solely based on psychoacoustical and physiological models to improve and to assess speech intelligibiltiy. The initial listening test revealed a discrepancy between the objective and subjective assessement of speech intelligibility, which suggests a review of the objective function. Once the objective function is in accordance with the individual perception of speech intelligibility, the presented framework could be applied in the optimization of all complex speech processors and therewith accelerate their assessment and application.
ER  -