• Corpus ID: 204785216

Effects of personalizing hearing-aid parameter settings using a real-time machine-learning approach

@inproceedings{Jensen2019EffectsOP,
  title={Effects of personalizing hearing-aid parameter settings using a real-time machine-learning approach},
  author={Niels S{\o}gaard Jensen and Laura Winther Balling and Jens Brehm Nielsen},
  year={2019}
}
In most hearing-aid fittings, amplification is prescribed by a fitting rationale that uses the audiogram as the main input. This approach may fail in situations where the user’s listening intention deviates from that assumed by the rationale. This shortcoming motivated a new commercially available method to self-adjust hearing-aid parameters while in a specific situation. The method is based on machine-learning algorithms that estimate the setting that optimizes user satisfaction based on user… 

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