Ann Spriet

Adaptive feedback cancellation in hearing aids with linear prediction of the desired signal

The standard continuous adaptation feedback cancellation algorithm for feedback suppression in hearing aids suffers from a large model error or bias if the received sound signal is spectrally colored. To reduce the bias in the feedback path estimate, we propose adaptive feedback cancellation techniques that are based on a closed-loop identification of the feedback path as well as the (auto-regressive) modeling of the desired signal. In general, both models are not simultaneously identifiable in the closed-loop system at hand. We show that-under certain conditions, e.g., if a delay is inserted in the forward path-identification of both models is indeed possible. Two classes of adaptive procedures for identifying the desired signal model and the feedback path are derived: a two-channel identification method as well as a prediction error method. In contrast to the two-channel identification method, the prediction error method allows use of different adaptation schemes for the feedback path and for the desired signal model and, hence, is found to be preferable for highly nonstationary sound signals. Simulation results demonstrate that the proposed techniques outperform the standard continuous adaptation algorithm if the conditions for identifiability are satisfied.

Frequency-domain criterion for the speech distortion weighted multichannel Wiener filter for robust noise reduction

Recently, a generalized multi-microphone noise reduction scheme, referred to as the spatially pre-processed speech distortion weighted multichannel Wiener filter (SP-SDW-MWF), has been presented. This scheme consists of a fixed spatial pre-processor and a multichannel adaptive noise canceler (ANC) optimizing the SDW-MWF cost function. By taking speech distortion explicitly into account in the design criterion of the multichannel ANC, the SP-SDW-MWF adds robustness to the standard generalized sidelobe canceler (GSC). In this paper, we present a multichannel frequency-domain criterion for the SDW-MWF, from which several - existing and novel - adaptive frequency-domain algorithms can be derived. The main difference between these adaptive algorithms consists in the calculation of the step size matrix (constrained vs. unconstrained, block-structured vs. diagonal) used in the update formula for the multichannel adaptive filter. We investigate the noise reduction performance, the robustness and the tracking performance of these adaptive algorithms, using a perfect voice activity detection (VAD) mechanism and using an energy-based VAD. Using experimental results with a small-sized microphone array in a hearing aid, it is shown that the SP-SDW-MWF is more robust against signal model errors than the GSC, and that the block-structured step size matrix gives rise to a faster convergence and a better tracking performance than the diagonal step size matrix, only at a slightly higher computational cost.

Speech understanding in background noise with the two-microphone adaptive beamformer BEAM in the Nucleus Freedom Cochlear Implant System.

Objective: This paper evaluates the benefit of the two-microphone adaptive beamformer BEAM™ in the Nucleus Freedom™ cochlear implant (CI) system for speech understanding in background noise by CI users. Design: A double-blind evaluation of the two-microphone adaptive beamformer BEAM and a hardware directional microphone was carried out with five adult Nucleus CI users. The test procedure consisted of a pre- and post-test in the lab and a 2-wk trial period at home. In the pre- and post-test, the speech reception threshold (SRT) with sentences and the percentage correct phoneme scores for CVC words were measured in quiet and background noise at different signal-to-noise ratios. Performance was assessed for two different noise configurations (with a single noise source and with three noise sources) and two different noise materials (stationary speech-weighted noise and multitalker babble). During the 2-wk trial period at home, the CI users evaluated the noise reduction performance in different listening conditions by means of the SSQ questionnaire. In addition to the perceptual evaluation, the noise reduction performance of the beamformer was measured physically as a function of the direction of the noise source. Results: Significant improvements of both the SRT in noise (average improvement of 5–16 dB) and the percentage correct phoneme scores (average improvement of 10–41%) were observed with BEAM compared to the standard hardware directional microphone. In addition, the SSQ questionnaire and subjective evaluation in controlled and real-life scenarios suggested a possible preference for the beamformer in noisy environments. Conclusions: The evaluation demonstrates that the adaptive noise reduction algorithm BEAM in the Nucleus Freedom CI-system may significantly increase the speech perception by cochlear implantees in noisy listening conditions. This is the first monolateral (adaptive) noise reduction strategy actually implemented in a mainstream commercial CI.

Spatially pre-processed speech distortion weighted multi-channel Wiener filtering for noise reduction

In this paper we establish a generalized noise reduction scheme, called the Spatially Pre-processed Speech Distortion Weighted Multi-channel Wiener filter (SP-SDW-MWF), that encompasses the Generalized Sidelobe Canceller (GSC) and a recently developed Multi-channel Wiener Filtering (MWF) technique as extreme cases and allows for in-between solutions. Compared to the widely studied GSC with Quadratic Inequality Constraint (QIC-GSC), the SP-SDW-MWF achieves a better noise reduction performance, for a given maximum speech distortion level.

Adaptive feedback cancellation in hearing aids

In general feedback cancellation setups, standard adaptive filtering techniques fail to provide a reliable feedback path estimate if the desired signal is spectrally colored because of the presence of a closed signal loop. In this paper, several approaches for improving the estimation accuracy of the adaptive feedback canceller in hearing aids will be reviewed, including constrained adaptation and bandlimited adaptation of the feedback canceller as well as adaptation with the prediction-error method (PEM) using a fixed or adaptive model of the desired signal. Partitioned-block frequency-domain implementations of these algorithms will be compared for acoustic feedback paths measured in two commercial behind-the-ear hearing aids. In addition, it is shown that the tracking performance of the PEM-based feedback canceller with adaptive signal model can be improved by the so-called shadow filter approach known from echo cancellation.

Robustness analysis of multichannel Wiener filtering and generalized sidelobe cancellation for multimicrophone noise reduction in hearing aid applications

For small-sized arrays such as hearing aids, noise reduction is obtained at the expense of an increased sensitivity to errors in the assumed signal model, i.e., microphone mismatch, variations in speaker and microphone positions, reverberation. However, the noise reduction algorithm should still be robust, i.e., insensitive to small signal model errors. In this paper, we evaluate the robustness of the Generalized Sidelobe Canceller (GSC) and a recently developed Multichannel Wiener Filtering (MWF) technique for hearing aid applications both analytically and experimentally. The analysis reveals that robustness of the GSC is especially crucial in complicated noise scenarios and that microphone mismatch is particularly harmful to the GSC, even when the adaptive noise canceller is adapted during noise only. Hence, a constraint on the noise sensitivity of the GSC is essential, at the expense of less noise reduction. The MWF on the other hand, is not affected by microphone mismatch and has a potential benefit over the robust GSC with noise sensitivity constraint. However, the MWF is sensitive to the estimation accuracy of the second order statistics of speech and noise so that its benefit may be lost in nonstationary noise scenarios.

Feedback Control in Hearing Aids

Acoustic feedback limits the maximum amplification that can be used in a hearing aid without making it unstable. This chapter gives an overview of existing techniques for feedback suppression and, in particular, adaptive feedback cancellation. Because of the presence of a closed signal loop, standard adaptive filtering techniques for open-loop systems fail to provide a reliable feedback path estimate if the desired signal is spectrally colored. Several approaches for improving the estimation accuracy of the adaptive feedback canceller will be reviewed and evaluated for acoustic feedback paths measured in a commercial behind-the-ear hearing aid.

Speech Distortion Weighted Multichannel Wiener Filtering Techniques for Noise Reduction

In many speech communication applications a microphone array is available nowadays, such that multi-microphone speech enhancement techniques can be used instead of single-microphone speech enhancement techniques. A wellknown multi-microphone speech enhancement technique is the generalized sidelobe canceller (GSC), which is however quite sensitive to signal model errors, such as microphone mismatch. This chapter discusses a more robust technique called the spatially pre-processed speech distortion weighted multichannel Wiener filter (SPSDW-MWF), which takes speech distortion due to signal model errors explicitly into account in its design criterion, and which encompasses the standard GSC as a special case. In addition, a novel frequency-domain criterion for the SDW-MWF is presented, from which several — existing and novel — adaptive frequency-domain algorithms can be derived for implementing the SDW-MWF. The noise reduction performance and the robustness of these adaptive algorithms is investigated for a hearing aid application. Using experimental results with a small-sized microphone array, it is shown that the SP-SDW-MWF is more robust against signal model errors than the GSC, both in stationary and in changing noise scenarios.

The impact of speech detection errors on the noise reduction performance of multi-channel Wiener filtering and generalized sidelobe cancellation

In contrast to the Generalized Sidelobe Canceller (GSC), the noise reduction performance of a recently developed multi-channel Wiener filter (MWF) technique does not depend on the validity of a priori assumptions about the signal model. This provides a potential benefit of the MWF over the GSC. However, both techniques also rely on a speech detection algorithm. In this paper, we analyze the average effect of speech detection errors on the performance of the GSC and the MWF both theoretically and experimentally. In the GSC case, it is the simultaneous presence of signal model errors and speech detection errors that affects performance. Incorporating a constraint on the noise sensitivity of the GSC limits the drastic impact of speech detection errors at the expense of reduced noise reduction performance. It is shown that the MWF preserves its benefit over the GSC for a reasonable speech detection error rate of 20% or less, even when the GSC is supplied with a noise sensitivity constraint. Real data experiments confirm the theoretical results.

Stochastic gradient-based implementation of spatially preprocessed speech distortion weighted multichannel Wiener filtering for noise reduction in hearing aids

Recently, a generalized noise reduction scheme has been proposed, called the Spatially Preprocessed, Speech Distortion Weighted, Multichannel Wiener Filter (SP-SDW-MWF). It encompasses the Generalized Sidelobe Canceller (GSC) and a multichannel Wiener filtering technique as extreme cases. Compared with the widely studied GSC with Quadratic Inequality Constraint (QIC-GSC), the SP-SDW-MWF achieves a better noise reduction performance for a given maximum speech distortion level. We develop a low-cost, stochastic gradient implementation of the SP-SDW-MWF. To speed up convergence and reduce computational complexity, the algorithm is implemented in the frequency domain. Experimental results with a behind-the-ear hearing aid show that the proposed frequency-domain stochastic gradient algorithm preserves the benefit of the exact SP-SDW-MWF over the QIC-GSC, while its computational cost is comparable to the least mean square-based scaled projection algorithm for QIC-GSC.