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This paper proposes a novel technique to improve a spectral statistical filter for speech enhancement, to be applied in wearable hearing devices such as hearing aids. The proposed method is implemented considering a 32-channel uniform polyphase discrete Fourier transform filter bank, for which the overall algorithm processing delay is 8 ms in accordance with the hearing device requirements. The proposed speech enhancement technique, which exploits the concepts of both non-negative sparse coding (NNSC) and spectral statistical filtering, provides an online unified framework to overcome the problem of residual noise in spectral statistical filters under noisy environments. First, the spectral gain attenuator of the statistical Wiener filter is obtained using the a priori signal-to-noise ratio (SNR) estimated through a decision-directed approach. Next, the spectrum estimated using the Wiener spectral gain attenuator is decomposed by applying the NNSC technique to the target speech and residual noise components. These components are used to develop an NNSC-based Wiener spectral gain attenuator to achieve enhanced speech. The performance of the proposed NNSC-Wiener filter was evaluated through a perceptual evaluation of the speech quality scores under various noise conditions with SNRs ranging from -5 to 20 dB. The results indicated that the proposed NNSC-Wiener filter can outperform the conventional Wiener filter and NNSC-based speech enhancement methods at all SNRs.

In this study, a speech enhancing neural network (NN) is proposed, which is designed for monaural auditory devices, specifically designed for use in hearing aids. Herein, a 32‐channel auditory filterbank (FB) is first implemented with an algorithm processing delay of 8 ms, which is tailored to meet the requirements of auditory devices. The proposed method primarily aims to integrate a denoising NN within the analysis phase of a uniform polyphase discrete Fourier transform (DFT) FB, aimed at enhancing speech within each band. For the denoising model, complex‐valued convolutional NNs have been applied, specifically targeting the restoration of speech phase information based on the spectral components of the DFT. A multi‐loss method is introduced, which is designed to further account for the loss of analysed speech signals within the split bands during the training process, leveraging the DFT FB strategy. To evaluate the efficacy of the proposed method, objective assessments of speech intelligibility and quality scores are conducted under various noise conditions. The results demonstrate that the proposed method can outperform the existing method across all types of noise. [ABSTRACT FROM AUTHOR]

This paper proposes a technique for improving statistical-model-based voice activity detection (VAD) in noisy environments to be applied in an auditory hearing aid. The proposed method is implemented for a uniform polyphase discrete Fourier transform filter bank satisfying an auditory device time latency of 8 ms. The proposed VAD technique provides an online unified framework to overcome the frequent false rejection of the statistical-model-based likelihood-ratio test (LRT) in noisy environments. The method is based on the observation that the sparseness of speech and background noise cause high false-rejection error rates in statistical LRT-based VAD—the false rejection rate increases as the sparseness increases. We demonstrate that the false-rejection error rate can be reduced by incorporating likelihood-ratio order statistics into a conventional LRT VAD. We confirm experimentally that the proposed method relatively reduces the average detection error rate by 15.8% compared to a conventional VAD with only minimal change in the false acceptance probability for three different noise conditions whose signal-to-noise ratio ranges from 0 to 20 dB. [ABSTRACT FROM AUTHOR]

Algorithms that are used in real-time hearing devices, such as hearing aids or cochlear implants, need to work with a short time latency in the order of 10 ms and must be efficient enough to run on battery power. Furthermore, in order to evaluate ecological validity such algorithms often need to be validated in the development stage using real-time processing of environmental sounds. However, researchers often lack the ability to access to the technology and they use offline schemes instead. In order to allow more and less specialised researchers access to the necessary technology and make it as easy and straightforward to develop real-time algorithm, we have developed an auditory real-time processing algorithm platform (“Open Development Platform”, ODP). The ODP is designed to help the development from existing offline algorithms to novel real-time algorithms by introducing a clear set of rules for development and evaluation. ODP has a software component that works in MATLAB/Simulink and a hardware component that works on the Speedgoat® Target machine. We also explain several algorithms from the available library that have low latency and low power consumption: filter bank, envelop power estimator, frequency shifter, and adaptive feedback canceller. As example of a complete project, we demonstrate here, how they can be combined to a fully functional hearing aid for research purposes. [ABSTRACT FROM AUTHOR]