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A DNN-based hearing-aid strategy for real-time processing : one size fits all

Fotios Drakopoulos (UGent) , Arthur Van Den Broucke (UGent) and Sarah Verhulst (UGent)
(2023) ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
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Abstract
Although hearing aids (HAs) can compensate for elevated hearing thresholds using sound amplification, they often fail to restore auditory perception in adverse listening conditions. To achieve robust treatment outcomes for diverse HA users, we use a differentiable framework that can compensate for impaired auditory processing based on a biophysically realistic and personalisable auditory model. Here, we present a deep-neural-network (DNN) HA processing strategy that can provide individualised sound processing for the audiogram of a listener using a single model architecture. The DNN architecture was trained to compensate for different audiogram inputs and was able to enhance simulated responses and intelligibility even for audiograms that were not part of training. Our multi-purpose HA model can be used for different individuals and can process audio inputs of 3.2 ms in <0.5 ms, thus paving the way for precise DNN-based treatments of hearing loss that can be embedded in hearing devices.

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Citation

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MLA
Drakopoulos, Fotios, et al. “A DNN-Based Hearing-Aid Strategy for Real-Time Processing : One Size Fits All.” ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2023, doi:10.1109/icassp49357.2023.10094887.
APA
Drakopoulos, F., Van Den Broucke, A., & Verhulst, S. (2023). A DNN-based hearing-aid strategy for real-time processing : one size fits all. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Presented at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023), Rhodes Island, Greece. https://doi.org/10.1109/icassp49357.2023.10094887
Chicago author-date
Drakopoulos, Fotios, Arthur Van Den Broucke, and Sarah Verhulst. 2023. “A DNN-Based Hearing-Aid Strategy for Real-Time Processing : One Size Fits All.” In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. https://doi.org/10.1109/icassp49357.2023.10094887.
Chicago author-date (all authors)
Drakopoulos, Fotios, Arthur Van Den Broucke, and Sarah Verhulst. 2023. “A DNN-Based Hearing-Aid Strategy for Real-Time Processing : One Size Fits All.” In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. doi:10.1109/icassp49357.2023.10094887.
Vancouver
1.
Drakopoulos F, Van Den Broucke A, Verhulst S. A DNN-based hearing-aid strategy for real-time processing : one size fits all. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2023.
IEEE
[1]
F. Drakopoulos, A. Van Den Broucke, and S. Verhulst, “A DNN-based hearing-aid strategy for real-time processing : one size fits all,” in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023.
@inproceedings{01HZHD15W7HXCZ8B6D46VXQ4CC,
  abstract     = {{Although hearing aids (HAs) can compensate for elevated hearing thresholds using sound amplification, they often fail to restore auditory perception in adverse listening conditions. To achieve robust treatment outcomes for diverse HA users, we use a differentiable framework that can compensate for impaired auditory processing based on a biophysically realistic and personalisable auditory model. Here, we present a deep-neural-network (DNN) HA processing strategy that can provide individualised sound processing for the audiogram of a listener using a single model architecture. The DNN architecture was trained to compensate for different audiogram inputs and was able to enhance simulated responses and intelligibility even for audiograms that were not part of training. Our multi-purpose HA model can be used for different individuals and can process audio inputs of 3.2 ms in <0.5 ms, thus paving the way for precise DNN-based treatments of hearing loss that can be embedded in hearing devices.}},
  author       = {{Drakopoulos, Fotios and Van Den Broucke, Arthur and Verhulst, Sarah}},
  booktitle    = {{ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
  isbn         = {{9781728163277}},
  issn         = {{2379-190X}},
  language     = {{eng}},
  location     = {{Rhodes Island, Greece}},
  pages        = {{5}},
  publisher    = {{IEEE}},
  title        = {{A DNN-based hearing-aid strategy for real-time processing : one size fits all}},
  url          = {{http://doi.org/10.1109/icassp49357.2023.10094887}},
  year         = {{2023}},
}
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