Eghart Fischer

Signal processing in high-end hearing aids: state of the art, challenges, and future trends

The development of hearing aids incorporates two aspects, namely, the audiological and the technical point of view. The former focuses on items like the recruitment phenomenon, the speech intelligibility of hearing-impaired persons, or just on the question of hearing comfort. Concerning these subjects, different algorithms intending to improve the hearing ability are presented in this paper. These are automatic gain controls, directional microphones, and noise reduction algorithms. Besides the audiological point of view, there are several purely technical problems which have to be solved. An important one is the acoustic feedback. Another instance is the proper automatic control of all hearing aid components by means of a classification unit. In addition to an overview of state-of-the-art algorithms, this paper focuses on future trends.

Retrieval of individualized head-related transfer functions for hearing aid applications

The capability of modern hearing aids to provide hearing-impaired humans with enhanced signals, which ultimately leads to an increased speech intelligibility, may benefit from fitting the device for each subject individually. This ideally also involves the exploitation of Head-Related Impulse Responses (HRIRs). However, HRIRs vary from person to person and thus require tedious measurements for each individual. In this work, we investigate two approaches which aim at speeding up the HRIR acquisition procedure. These are continuous measurements and interpolation, where Dynamic Time Warping (DTW) as well as linear interpolation of the magnitude and phase responses are considered. In contrast to related publications, the continuous HRIR measurements are not performed in anechoic environments here. The quality of the obtained HRIRs is assessed by means of the system mismatch and the proposed error of relative transfer functions. Both measures reveal that continuous HRIR measurements are on average much more capable than the investigated interpolation approaches, and they furthermore provide a more uniform performance for different source directions.

Efficient estimation of inter-subband speech correlations

We propose an approach to compute the inter-subband correlation (ISBC) of noisy speech signals to distinguish between speech and noise segments in the time-frequency plane. The proposed spectral correlation estimator provides information about the input signal which can be used to derive a binary mask or the speech-presence probability. Unlike other approaches it does not require an estimate of the noise power. To this end we analyse a received noisy speech signal in the modulation domain and identify similarly modulated subband signals within a range of modulation frequencies that are typical for speech signals. Based on this pre-processing step, we identify a single reference subband that most likely contains speech and estimate the spectral correlations with respect to this reference band. The algorithm proposed in this paper aims at a very low computational complexity which makes it suitable for hearing aids.