Creating Clarity in Noisy Environments by Using Deep Learning in Hearing Aids

• Published in: Seminars in hearing Vol. 42; no. 3; pp. 260 - 281
• Main Authors: Andersen, Asger Heidemann, Santurette, Sébastien, Pedersen, Michael Syskind, Alickovic, Emina, Fiedler, Lorenz, Jensen, Jesper, Behrens, Thomas
• Format: Journal Article
• Language: English
• Published: 333 Seventh Avenue, 18th Floor, New York, NY 10001, USA Thieme Medical Publishers, Inc 01.08.2021; Thieme Medical Publishers Inc
• Series: Hearing Aid Technology to Improve Speech Intelligibility in Noise
• Subjects: Algorithms; Artificial intelligence; Deep learning; Hearing aids; Intelligibility; Noise; Review; Review Article; Signal to noise ratio; noise reduction; directionality; beamforming; postfiltering
• ISSN: 0734-0451; 1098-8955
• DOI: 10.1055/s-0041-1735134

Abstract Hearing aids continue to acquire increasingly sophisticated sound-processing features beyond basic amplification. On the one hand, these have the potential to add user benefit and allow for personalization. On the other hand, if such features are to benefit according to their potential, they require clinicians to be acquainted with both the underlying technologies and the specific fitting handles made available by the individual hearing aid manufacturers. Ensuring benefit from hearing aids in typical daily listening environments requires that the hearing aids handle sounds that interfere with communication, generically referred to as “noise.” With this aim, considerable efforts from both academia and industry have led to increasingly advanced algorithms that handle noise, typically using the principles of directional processing and postfiltering. This article provides an overview of the techniques used for noise reduction in modern hearing aids. First, classical techniques are covered as they are used in modern hearing aids. The discussion then shifts to how deep learning, a subfield of artificial intelligence, provides a radically different way of solving the noise problem. Finally, the results of several experiments are used to showcase the benefits of recent algorithmic advances in terms of signal-to-noise ratio, speech intelligibility, selective attention, and listening effort.