Thomas Rohdenburg

Robustness Analysis of Binaural Hearing Aid Beamformer Algorithms by Means of Objective Perceptual Quality Measures

In this contribution different microphone array-based noise reduction schemes for hearing aids are suggested and compared in terms of their performance, signal quality and robustness against model errors. The algorithms all have binaural output and are evaluated using objective perceptual quality measures [1, 2, 3]. It has been shown earlier that these measures are able to predict subjective data that is relevant for the assessment of noise reduction algorithms. The quality measures showed clearly that fixed beamformers designed with head models were relatively robust against steering errors whereas for the adaptive beamformers tested in this study the robustness was limited and the benefit due to higher noise reduction depended on the noise scenario and the reliability of a direction of arrival estimation. Furthermore, binaural cue distortions introduced by the different binaural output strategies could be identified by the binaural speech intelligibility measure [3] even in case monaural quality values were similar. Thus, this perceptual quality measure seems to be suitable to discover the benefit that the listener might have from the effect of spatial unmasking.

Microphone position optimization for planar superdirective beamforming

The performance of a fixed beamformer highly depends on the position of the microphones in the array. In this paper, different heuristic optimisation approaches for arbitrary planar arrays and an exhaustive search approach for structured array geometries are presented to optimise the microphone positions for a superdirective beamformer, aiming at maximizing the mean directivity index for several steering angles of interest. Through the derivation of an upper bound on the achievable performance, it is shown that the proposed approaches generate configurations with a near-optimal performance. In addition, the theoretical results are validated using real measurements, demonstrating the practical usability of the proposed methods.

Objective perceptual quality assessment for self-steering binaural hearing aid microphone arrays

In this study a self-steering beamformer with binaural output for a head-worn microphone array is investigated in simulated and real- world conditions. The influence of the underlying sound propagation model on the estimation accuracy of the direction of arrival (DOA) estimation algorithm and the overall performance of the combined DOA-beamformer-system is evaluated. For this, technical performance measures as well as objective quality measures based on perceptual models of the auditory system are used. The self-steering beamformer showed better performance than a beamformer with fixed look-direction for SNR values above -2 dB if the propagation model includes at least a coarse head model.

Perception‐model‐based sound quality prediction for hearing aids: Big effect, small effect, and the optimization of noise reduction algorithms

Objective sound quality models can be used to optimize hearing aid algorithms and their respective parameter settings by numerically predicting the subjective sound quality assessed by humans without having to perform quality measurements with humans concurrently. The big effect prediction considers how the (individual) hearing loss affects the aided or unaided perceived sound quality. The small effect prediction aims at subtle differences at a given amplification level of the hearing aid in order to compare processing parameters. This talk will concentrate on benchmarking different types of objective prediction methods both for big effect and small effect conditions using a wide range of signals and processing conditions, respectively. In addition, the performance of PEMO—Q, an auditory‐model‐based quality prediction scheme developed at Oldenburg University will be discussed, which has been modified to include the effect of hearing impairment and the adaptation to different kinds of test data sets. We wi...

Tutorial hearing aid algorithms

The normal-hearing system extracts monaural and binaural features from the signals at the left and right ears in order to separate and classify sound sources. Robustness of source extraction is achieved by exploiting redundancies in the source signals (auditory scene analysis, ASA). ASA is closely related to the “Cocktail Party Effect”, i.e., the ability of normal-hearing listeners to perceive speech in adverse conditions at low signal-to-noise ratios. Hearing-impaired people show a reduced ability to understand speech in noisy environments, stressing the necessity to incorporate noise reduction schemes into hearing aids. Several algorithms for monaural, binaural and multichannel noise reduction have been proposed, which aim at increasing speech intelligibility in adverse conditions. A summary of recent algorithms including directional microphones, beamformers, monaural noise reduction and perceptual model-based binaural schemes will be given. In practice, these schemes were shown to be much less efficient than the normal-hearing system in acoustically complex environments characterized by diffuse noise and reverberation. One reason might be that redundancies in the source signals exploited by the hearing system are not used so far by noise reduction algorithms. Novel multidimensional statistical filtering algorithms are introduced that might fill this gap in the future. Noise reduction schemes often require high computational load which results in a high power consumption. To reduce the computational expense one promising approach could be to reduce the numerical precision in specific parts of the algorithm or to replace costly parts by computationally simpler functions. This might lead to additional distortion in the signal that reduces the perceived audio signal quality. Quality Assessment is needed to control the negative effects of power optimization in the algorithms. However, subjective listening tests are time-consuming and cost-intensive and therefore inappropriate for tracking small changes in the algorithm. Objective quality measures based on auditory models that can predict subjective ratings are needed. We introduce a quality test-bench for noise reduction schemes that helps the developer to objectively assess the effects of power optimization on audio quality. Hence, a compromise between audio quality degradation and power consumption can be obtained in a fast and cost-efficient procedure that is based on auditory models. For more information and related literature please refer to: “http://www.physik.uni-oldenburg.de/Docs/medi/publhtml/publdb.byyeardoctype.html”