Low Latency Speech Enhancement for Hearing Aids Using Deep Filtering

• Published in: IEEE/ACM transactions on audio, speech, and language processing Vol. 30; pp. 2716 - 2728
• Main Authors: Schroter, Hendrik, Rosenkranz, Tobias, Escalante-B, Alberto-N., Maier, Andreas
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial neural networks; Customer relationship management; Deep learning; deep neural network; Filter banks; Frequency response; Hearing aid; Hearing aids; Machine learning; Noise control; Noise measurement; Noise reduction; Signal to noise ratio; Spectrograms; Speech enhancement; Speech processing
• ISSN: 2329-9290; 2329-9304
• DOI: 10.1109/TASLP.2022.3198548

Noise reduction is an important feature supporting hearing aid (HA) users in their daily routines and is thus included in most commercially available devices. Latency requirements of HAs require short processing windows resulting in a poor frequency resolution in the whole processing chain including noise reduction. Previous studies have shown that deep neural network (DNN) based algorithms outperform conventional noise reduction algorithms especially for non-stationary noises. This study explores a DNN based noise reduction method using deep filtering targeted for wideband spectrograms given the employed HA filter bank. That is, we predict complex filter coefficients that are linearly applied to the noisy spectrum. We assess different filter sizes over time and frequency axis, and provide evidence for a superior performance over a complex ratio mask. Furthermore, we introduce a frequency response loss that operates on a per-frequency-band basis to fully utilize the deep filtering concept. We objectively demonstrate on-par performance with related state-of-the-art deep learning methods and show in a subjective user study that our method is perceptually preferred to existing HA noise reduction algorithms.