Henning F. Schepker

Listening effort and speech intelligibility in listening situations affected by noise and reverberation.

This study compared the combined effect of noise and reverberation on listening effort and speech intelligibility to predictions of the speech transmission index (STI). Listening effort was measured in normal-hearing subjects using a scaling procedure. Speech intelligibility scores were measured in the same subjects and conditions: (a) Speech-shaped noise as the only interfering factor, (b) + (c) fixed signal-to-noise ratios (SNRs) of 0 or 7 dB and reverberation as detrimental factors, and (d) reverberation as the only detrimental factor. In each condition, SNR and reverberation were combined to produce STI values of 0.17, 0.30, 0.43, 0.57, and 0.70, respectively. Listening effort always decreased with increasing STI, thus enabling a rough prediction, but a significant bias was observed indicating that listening effort was lower in reverberation only than in noise only at the same STI for one type of impulse responses. Accordingly, speech intelligibility increased with increasing STI and was significantly better in reverberation only than in noise only at the same STI. Further analyses showed that the broadband reverberation time is not always a good estimate of speech degradation in reverberation and that different speech materials may differ in their robustness toward detrimental effects of reverberation.

Improving adaptive feedback cancellation in hearing aids using an affine combination of filters

In adaptive feedback cancellation an adaptive filter is used to model the acoustic feedback path between the hearing aid loudspeaker and the microphone. An important parameter for adaptive filters is the step-size, providing a trade-off between fast convergence and low steady-state misalignment. In order to achieve both fast convergence as well as low steady-state misalignment, it has been proposed to use an affine combination scheme of two filters operating with different step-sizes. In this paper we apply such an affine combination scheme to the acoustic feedback cancellation problem in hearing aids. We show that for speech signals a time-domain affine combination scheme yields a biased solution. To reduce this bias we propose to use a partitioned-block frequency-domain affine combination scheme. Experimental results using measured acoustic feedback paths show that in terms of misalignment and added stable gain the proposed adaptive feedback cancellation system outperforms a system that only uses a single adaptive filter with either of the fixed step-sizes used for the affine combination scheme.

Reciprocal measurement of acoustic feedback paths in hearing aids

A reciprocal measurement procedure to measure the acoustic feedback path in hearing aids is investigated. The advantage of the reciprocal measurement compared to the direct measurement is a significantly reduced sound pressure in the ear. The direct and reciprocal measurements are compared using measurements on a dummy head with adjustable ear canals, different earmolds, and variations in the outer sound field. The results show that the reciprocal measurement procedure can be used to obtain plausible feedback paths, while reducing the sound pressure in the ear canal by 30 to 40 dB.

Speech-in-noise enhancement using amplification and dynamic range compression controlled by the speech intelligibility index

In many speech communication applications, such as public address systems, speech is degraded by additive noise, leading to reduced speech intelligibility. In this paper a pre-processing algorithm is proposed that is capable of increasing speech intelligibility under an equal-power constraint. The proposed AdaptDRC algorithm comprises two time- and frequency-dependent stages, i.e., an amplification stage and a dynamic range compression stage that are both dependent on the Speech Intelligibility Index (SII). Experiments using two objective measures, namely, the extended SII and the short-time objective intelligibility measure (STOI), and a formal listening test were conducted to compare the AdaptDRC algorithm with a modified version of a recently proposed algorithm in three different noise conditions (stationary car noise and speech-shaped noise and non-stationary cafeteria noise). While the objective measures indicate a similar performance for both algorithms, results from the formal listening test indicate that for the two stationary noises both algorithms lead to statistically significant improvements in speech intelligibility and for the non-stationary cafeteria noise only the proposed AdaptDRC algorithm leads to statistically significant improvements. A comparison of both objective measures and results from the listening test shows high correlations, although, in general, the performance of both algorithms is overestimated.

MODELING THE COMMON PART OF ACOUSTIC FEEDBACK PATHS IN HEARING AIDS USING A POLE-ZERO MODEL

In adaptive feedback cancellation the computational complexity and the convergence speed are determined by the number of adaptive parameters used to model the acoustic feedback path. Therefore it has been proposed to reduce the number of adaptive parameters by modeling the feedback path as the convolution of a time-invariant common part and a time-varying variable part. While previous approaches have modeled the common part either using only poles or using only zeros, in this paper we propose to use a common pole-zero model and present an iterative method to compute the common poles and zeros. Using measured acoustic feedback paths from a two-microphone behind-the-ear hearing aid it is shown that the proposed model enables either to increase the modeling accuracy given a fixed number of parameters of the variable part or to reduce the number of parameters of the variable part given a desired accuracy.

Perceived listening effort and speech intelligibility in reverberation and noise for hearing-impaired listeners

Abstract Objective: The purpose of this study was to assess perceived listening effort and speech intelligibility in reverberant and noisy conditions for hearing-impaired listeners for conditions that are similar according to the speech transmission index (STI). Design: Scaled listening effort was measured in four different conditions at five different STI generated using various relative contributions of noise and reverberant interferences. Intelligibility was measured for a subset of conditions. Study sample: Twenty mildly to moderately hearing-impaired listeners. Results: In general, listening effort decreased and speech intelligibility increased with increasing STI. For simulated impulse responses consisting of white Gaussian noise exponentially decaying in time, a good agreement between conditions of different relative contributions of noise and reverberation was found. For real impulse responses, the STI slightly overestimated the effect of reverberation on the perceived listening effort and underestimated its effect on speech intelligibility. Including the average hearing loss in the calculation of the STI led to a better agreement between STI predictions and subjective data. Conclusion: Speech intelligibility and listening effort provide complementary tools to evaluate speech perception over a broad range of acoustic scenarios. In addition, when incorporating hearing loss information the STI provides a rough prediction of listening effort in these acoustic scenarios.

A semidefinite programming approach to min-max estimation of the common part of acoustic feedback paths in hearing aids

The convergence speed and the computational complexity of adaptive feedback cancellation algorithms both depend on the number of adaptive parameters used to model the acoustic feedback paths. To reduce the number of adaptive parameters it has been proposed to decompose the acoustic feedback paths as the convolution of a time-invariant common part and time-varying variable parts. Instead of estimating all parameters of the common and variable parts by minimizing the misalignment using a least-squares cost function, in this paper we propose to formulate the parameter estimation problem as a min-max optimization problem aiming to maximize the maximum stable gain (MSG). We formulate the min-max optimization problem as a semidefinite program and use a constraint based on Lyapunov theory to guarantee stability of the estimated common pole-zero filter. Experimental results using measured acoustic feedback paths show that the proposed min-max optimization outperforms least-squares optimization in terms of the MSG. Furthermore, the results indicate that the proposed common part decomposition is able to increase the MSG and reduce the number of variable part parameters even for unknown feedback paths that were not included in the optimization. Simulation results using an adaptive feedback cancellation algorithm based on the prediction-error-method show that the convergence speed can be increased by using the proposed feedback path decomposition.

Estimation of the common part of acoustic feedback paths in hearing aids using iterative quadratic programming

In adaptive feedback cancellation the convergence speed and the computational complexity depend on the number of adaptive parameters used to model the acoustic feedback path. To improve the convergence speed and reduce the computational complexity, it has been proposed to model the acoustic feedback path as the convolution of a time-invariant common pole-zero part and a time-varying variable part. Previous approaches to estimate all the coefficients minimized the so-called equation-error which possibly suffers from poor estimation accuracy in the vicinity of prominent spectral regions, e.g., spectral peaks. In this paper we therefore propose to minimize the so-called output-error by using a Steiglitz-McBride-like iteration scheme. To ensure the stability of the estimated pole-zero filter a frequency domain constraint is used leading to a quadratic programming problem. Experimental results using measured impulse responses from a two-microphone behind-the-ear hearing aid show that the proposed estimation scheme outperforms the existing estimation scheme in terms of modeling accuracy.

Common part estimation of acoustic feedback paths in hearing aids optimizing maximum stable gain

The computational complexity and convergence speed of adaptive feedback cancellation algorithms depend on the number of adaptive parameters used to model the acoustic feedback path. To reduce the number of adaptive parameters it has been proposed to decompose the acoustic feedback path as the convolution of a (time-invariant) common part and a (time-varying) variable part. Typically the problem of estimating all the required coefficients has been formulated as a least-squares optimization problem. In contrast, in this paper we propose to formulate the estimation problem as a minmax optimization problem and show how this is associated with the maximum stable gain of a hearing aid. Experimental results using measured acoustic feedback paths from a two-microphone behind-the-ear hearing aid show that the proposed minmax optimization outperforms the least-squares optimization in terms of maximum stable gain. Furthermore, the robustness of proposed common part decomposition for different feedback paths is evaluated.

Null-steering beamformer for acoustic feedback cancellation in a multi-microphone earpiece optimizing the maximum stable gain

Commonly adaptive filters are used to reduce the acoustic feedback in hearing aids. While theoretically allowing for perfect cancellation of the feedback signal, in practice the adaptive filter solution is typically biased due to the closed-loop hearing aid system. In contrast to conventional behind-the-ear hearing aids, in this paper we consider an earpiece with multiple integrated microphones. For such an earpiece it has previously been proposed to use a fixed beamformer to reduce the acoustic feedback in the microphones which has been designed to minimize a least-squares cost function. In this paper we propose to design the beamformer by minimizing a min-max cost function which directly maximizes the maximum stable gain of the earpiece. Furthermore, we propose a robust extension of the min-max cost function maximizing the worst-case maximum stable gain over a set of acoustic feedback paths. Experimental results using measured acoustic feedback paths show that the feedback cancellation performance of the fixed beamformer can be considerably improved by minimizing the proposed min-max optimization problem, while maintaining a high perceptual quality of the incoming signal.