This work focuses on online dereverberation for hearing devices using the weighted prediction error (WPE) algorithm. WPE filtering requires an estimate of the target speech power spectral density (PSD). Recently deep neural networks (DNNs) have been used for this task. However, these approaches optimize the PSD estimate which only indirectly affects the WPE output, thus potentially resulting in limited dereverberation. In this paper, we propose an end-to-end approach specialized for online processing, that directly optimizes the dereverberated output signal. In addition, we propose to adapt it to the needs of different types of hearing-device users by modifying the optimization target as well as the WPE algorithm characteristics used in training. We show that the proposed end-to-end approach outperforms the traditional and conventional DNN-supported WPEs on a noise-free version of the WHAMR! dataset.

In this paper, a neural network-augmented algorithm for noise-robust online dereverberation with a Kalman filtering variant of the weighted prediction error (WPE) method is proposed.The filter stochastic variations are predicted by a deep neural network (DNN) trained end-to-end using the filter residual error and signal characteristics.The presented framework allows for robust dereverberation on a single-channel noisy reverberant dataset similar to WHAMR!.The Kalman filtering WPE introduces distortions in the enhanced signal when predicting the filter variations from the residual error only, if the target speech power spectral density is not perfectly known and the observation is noisy.The proposed approach avoids these distortions by correcting the filter variations estimation in a data-driven way, increasing the robustness of the method to noisy scenarios.Furthermore, it yields a strong dereverberation and denoising performance compared to a DNN-supported recursive least squares variant of WPE, especially for highly noisy inputs.

Despite many considerable technical advances in the field of hearing aids and cochlear implants, people using auditory prostheses still have major problems with speech understanding in the presence of interfering sounds and with directional hearing. Both abilities are dependent on sound stream segregation in real-world listening environments. In this paper, two timely and important issues related to sound stream segregation in auditory prostheses are addressed, namely, the coding of monaural and binaural cues. Several state-of-the-art signal processing algorithms used in cochlear implants (CIs) and in hearing aids (HAs) are introduced. A review is given of some recent proposals to improve temporal coding in monaural CIs, and of recent work to improve the transmission of binaural cues in both HAs, CIs, and combined acoustic and electric hearing (bimodal hearing). The ultimate aim is to improve speech and music perception, and, additionally, the preservation of binaural cues to preserve directional hearing.

Whereas state-of-the-art single-channel noise reduction algorithms for auditory prostheses demonstrate an appreciable suppression of the noise and improved speech quality, they are unable, thus far, to improve the intelligibility of noise-degraded speech signals. Alternative approaches to speech enhancement using a binary time-frequency mask have demonstrated substantial intelligibility improvements in low signal-to-noise-ratio (SNR) conditions under ideal settings, making this a promising research direction for auditory prostheses. These approaches exploit the sparsity and disjoint-ness of speech spectra in their short-time-frequency representation to preserve only the target-dominant time-frequency regions in the processed output. State-of-the-art noise reduction algorithms in contrast are soft-decision approaches which weight each time-frequency region in proportion to the prevailing SNR. However, the potential for intelligibility improvement using these approaches has not been examined systematically vis-à-vis the binary mask alternative. This contribution compares the performance of an ideal soft-decision system, exemplified by the ideal Wiener filter (IWF), and the ideal binary mask (IBM) for single-channel speech enhancement for auditory prostheses. To obtain results relevant to this application area, a (relatively) low spectral resolution, modelled using the Bark-spectrum scale, is used for both the IWF and the IBM. This spectral resolution is comparable to that being used in commercial hearing instruments. The comparison is in terms of potential for intelligibility improvement and resulting signal quality. Intelligibility tests carried out under various noise conditions and SNRs show that the IWF leads to higher intelligibility scores than the IBM in low SNR conditions. Under non-ideal parameter estimates, it is demonstrated that the IWF approach is also much less sensitive to estimation errors. Quality-wise, a preference for the IWF exists. This was evaluated using a two-stage, pair-wise preference-rating test.