Objective measures are widely used for the perceptual sound-quality evaluation of audio signal processing algorithms. Nevertheless, the use of subjective-evaluation measures remains relevant, in particular when application-specific objective measures are lacking. In this paper, we present a perceptual sound-quality evaluation of different algorithms for adaptive feedback cancellation (AFC), with both speech and music signals. Three algorithms are compared: the block normalized least mean squares algorithm, the prediction-error method (PEM) based frequency-domain adaptive filter, and the PEM-based frequency-domain Kalman filter (PEM-FDKF). The subjective evaluation results for the tested algorithms suggest that there is a large difference in statistical significance, and a corresponding large effect size, between the PEM-FDKF and the other algorithms, when using speech signals. A smaller statistical significance, and a lower effect size, is reported when using music signals. The subjective evaluation results are then compared with the results obtained with several objective measures. The correlation between subjective and objective scores shows that objective measures can be effectively used to predict the sound-quality degradation caused by acoustic feedback and AFC artifacts.

Adaptive filtering based feedback cancellation is a widespread approach to acoustic feedback control. However, traditional adaptive filtering algorithms have to be modified in order to work satisfactorily in a closed-loop scenario. In particular, the undesired signal correlation between the loudspeaker signal and the source signal in a closed-loop scenario is one of the major problems to address when using adaptive filters for feedback cancellation. Slow convergence speed and limited tracking capabilities are other important limitations to be considered. Additionally, computationally expensive algorithms as well as long delays should be avoided, for instance, in hearing aid applications, because of power constraints, important to extend battery life, and real-time implementations requirements, respectively. We present an algorithm combining good decorrelation properties, by means of the prediction-error method based signal prewhitening, fast convergence, good tracking behavior, and low computational complexity by means of the frequency-domain Kalman filter, and low delay by means of a partitioned-block implementation.

Traditionally, algorithms that attempt to significantly improve speech intelligibility in noise for cochlear implant (CI) users have met with limited success, particularly in the presence of a fluctuating masker. In the present study, a speech enhancement algorithm integrating an artificial neural network (NN) into CI coding strategies is proposed. The algorithm decomposes the noisy input signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the NN to produce an estimation of which CI channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is then used accordingly to retain a subset of channels for electrical stimulation, as in traditional n-of-m coding strategies. The proposed algorithm was tested with 10 normal-hearing participants listening to CI noise-vocoder simulations against a conventional Wiener filter based enhancement algorithm. Significant improvements in speech intelligibility in stationary and fluctuating noise were found over both unprocessed and Wiener filter processed conditions.

Using periodic auditory stimuli, it is possible to evoke so-called auditory steady-state responses (ASSRs) in the brain, which can be measured using electroencephalography (EEG). They can be used to objectively estimate frequency-specific hearing thresholds, which is especially useful for early hearing assessment in newborns. The main problem is the extremely low signal-to-noise ratio (SNR), necessitating long measurements of up to an hour for a full audiometric assessment. To speed up the detection, we apply a linear spatial filter to the multi-channel EEG measurements, resulting in a new 'virtual' channel with optimal SNR. To ensure robustness, we then consider a hybrid ASSR detection method in which the original EEG channels are complemented with this virtual channel. The addition of this virtual channel successfully speeds up the detection of ASSRs by over 15 %. Furthermore our method not only speeds up the detection, but also greatly improves its sensitivity, in particular in the (clinically most relevant) lowest SNR scenarios. This could help reduce the gap that still exists between behaviourally and objectively obtained hearing thresholds.

Binaural hearing aids allow for a wireless exchange of microphone signals between a left and a right device. A significant noise reduction performance improvement can be achieved compared to a monaural configuration (a single device) or a bilateral configuration (in which the devices work independently). In addition, the binaural localization cues, i.e. the Interaural Time Differences and Interaural Level Differences , can also be better preserved in a binaural procedure. It was previously proven that a binaural noise reduction procedure based on the Speech Distortion Weighted Multi-channel Wiener Filter (SDW-MWF) indeed preserves the speech localization cues, if all microphone signals can be exchanged. However, in practice, it may not be feasible to exchange all microphone signals between the devices, so that reduced-bandwidth SDW-MWF schemes (where only filtered combinations of microphone signals are exchanged) have to be utilized. This paper demonstrates that a straightforward reduced-bandwidth SDW-MWF scheme still preserves the speech ITD cues, but distorts the speech ILD cues, in a single speech source scenario. Novel reduced-bandwidth SDW-MWF schemes, which make use of a common spectral postfilter, are therefore introduced. Experiments in a reverberant environment demonstrate that the novel schemes reduce the ILD distortion, without severely degrading the noise reduction performance.

This paper presents low-rank approximation based multichannel Wiener filter algorithms for noise reduction in speech plus noise scenarios, with application in cochlear implants. In a single speech source scenario, the frequency-domain autocorrelation matrix of the speech signal is often assumed to be a rank-1 matrix, which then allows to derive different rank-1 approximation based noise reduction filters. In practice, however, the rank of the autocorrelation matrix of the speech signal is usually greater than one. Firstly, the link between the different rank-1 approximation based noise reduction filters and the original speech distortion weighted multichannel Wiener filter is investigated when the rank of the autocorrelation matrix of the speech signal is indeed greater than one. Secondly, in low input signal-to-noise-ratio scenarios, due to noise non-stationarity, the estimation of the autocorrelation matrix of the speech signal can be problematic and the noise reduction filters can deliver unpredictable noise reduction performance. An eigenvalue decomposition based filter and a generalized eigenvalue decomposition based filter are introduced that include a more robust rank-1, or more generally rank-R, approximation of the autocorrelation matrix of the speech signal. These noise reduction filters are demonstrated to deliver a better noise reduction performance especially in low input signal-to-noise-ratio scenarios. The filters are especially useful in cochlear implants, where more speech distortion and hence a more aggressive noise reduction can be tolerated.

Over the past decades, preschoolers emerged as frequent and experienced users of new digital media. As this trend continues, it is important for game designers to address the gratifications of this new gaming audience. Unfortunately, existing theoretical frameworks on meaningful gameplay for preschoolers are rare, outdated, or they lack a comprehensive validation. In this paper, we present a User Experience (UX) Laddering study to unveil the gameplay preferences of preschoolers, relying on five-year olds (n=25) as active research participants. The results of this study provide a set of meaningful and useful guidelines for future game designers, directed at this young target group.

This paper presents multichannel Wiener filtering-based algorithms for noise reduction in cochlear implants. In a single speech scenario, the autocorrelation matrix of the speech signal can be approximated by a rank-1 matrix. It is then possible to derive noise reduction filters that deliver improved signal-to-noise ratio performance. The link between these different filters is investigated here and an eigenvalue decomposition based algorithm is demonstrated to be more stable at low input signal-to-noise ratio compared to previous algorithms.

Despite many considerable technical advances in the field of hearing aids and cochlear implants, people using auditory prostheses still have major problems with speech understanding in the presence of interfering sounds and with directional hearing. Both abilities are dependent on sound stream segregation in real-world listening environments. In this paper, two timely and important issues related to sound stream segregation in auditory prostheses are addressed, namely, the coding of monaural and binaural cues. Several state-of-the-art signal processing algorithms used in cochlear implants (CIs) and in hearing aids (HAs) are introduced. A review is given of some recent proposals to improve temporal coding in monaural CIs, and of recent work to improve the transmission of binaural cues in both HAs, CIs, and combined acoustic and electric hearing (bimodal hearing). The ultimate aim is to improve speech and music perception, and, additionally, the preservation of binaural cues to preserve directional hearing.

Whereas state-of-the-art single-channel noise reduction algorithms for auditory prostheses demonstrate an appreciable suppression of the noise and improved speech quality, they are unable, thus far, to improve the intelligibility of noise-degraded speech signals. Alternative approaches to speech enhancement using a binary time-frequency mask have demonstrated substantial intelligibility improvements in low signal-to-noise-ratio (SNR) conditions under ideal settings, making this a promising research direction for auditory prostheses. These approaches exploit the sparsity and disjoint-ness of speech spectra in their short-time-frequency representation to preserve only the target-dominant time-frequency regions in the processed output. State-of-the-art noise reduction algorithms in contrast are soft-decision approaches which weight each time-frequency region in proportion to the prevailing SNR. However, the potential for intelligibility improvement using these approaches has not been examined systematically vis-à-vis the binary mask alternative. This contribution compares the performance of an ideal soft-decision system, exemplified by the ideal Wiener filter (IWF), and the ideal binary mask (IBM) for single-channel speech enhancement for auditory prostheses. To obtain results relevant to this application area, a (relatively) low spectral resolution, modelled using the Bark-spectrum scale, is used for both the IWF and the IBM. This spectral resolution is comparable to that being used in commercial hearing instruments. The comparison is in terms of potential for intelligibility improvement and resulting signal quality. Intelligibility tests carried out under various noise conditions and SNRs show that the IWF leads to higher intelligibility scores than the IBM in low SNR conditions. Under non-ideal parameter estimates, it is demonstrated that the IWF approach is also much less sensitive to estimation errors. Quality-wise, a preference for the IWF exists. This was evaluated using a two-stage, pair-wise preference-rating test.

This paper presents a binaural approach to integrated active noise control and noise reduction in hearing aids and aims at demonstrating that a binaural setup indeed provides significant advantages in terms of the number of noise sources that can be compensated for and in terms of the causality margins.

This paper focuses on speech enhancement in hearing aids and presents an integrated approach to active noise control and noise reduction which is based on an optimization over a zone-of-quiet generated by the active noise control. A basic integrated active noise control and noise reduction scheme has been introduced previously to tackle secondary path effects and effects of noise leakage through an open fitting. This scheme however, only takes the sound pressure at the ear canal microphone into account. For an integrated active noise control and noise reduction scheme to be efficient, it is desired to achieve active noise control at the eardrum which in practice is away from the ear canal microphone. In some cases, it can also be desired to achieve noise control over a zone not limited to a single point. Two different schemes are presented. The first scheme is based on a mean squared error criterion expressed at a remote point (RP) away from the ear canal microphone and the second scheme is based on an average mean squared error criterion over a desired zone-of-quiet. They are both compared experimentally with the original scheme for both active noise control and integrated active noise control and noise reduction, respectively. The remote-point approach then allows to restore the performance of the original scheme at the desired remote point while the zone-of-quiet approach allows to increase performance up to 3 dB on the desired zone-of-quiet.

The Speech Distortion Weighted Multichannel Wiener Filter (SDW-MWF) is a promising multi-microphone noise reduction technique, in particular for hearing aid applications. Its benefit over other techniques has been shown in several theoretical and experimental contributions. In theoretical studies, a single target speech source is commonly assumed, as this facilitates the analysis. In this contribution, we first prove that an algorithm, that implicitly assumes a single target speech source, is also more robust against estimation errors in the speech second order statistics, compared to a standard SDW-MWF algorithm. Secondly, as any SDW-MWF algorithm relies on a voice activity detector (VAD), a novel VAD-robust extension is also proposed. It is shown theoretically and through experiments with a realistic VAD that the new algorithm indeed achieves a good performance, even at low input SNR's where the VAD error rate is high.

In this paper, a multi-channel noise reduction algorithm is presented based on a Speech Distortion Weighted Multi-channel Wiener Filter (SDW-MWF) approach that incorporates a flexible weighting factor. A typical SDW-MWF uses a fixed weighting factor to trade-off between noise reduction and speech distortion without taking speech presence or speech absence into account. Consequently, the improvement in noise reduction comes at the cost of a higher speech distortion since the speech dominant segments and the noise dominant segments are weighted equally. Based on a two-state speech model with a noise-only and a speech+noise state, a solution is introduced that allows for a more flexible trade-off between noise reduction and speech distortion. Experimental results with hearing aid scenarios demonstrate that the proposed SDW-MWF incorporating the flexible weighting factor improves the signal-to-noise-ratio with lower speech distortion compared to a typical SDW-MWF and the SDW-MWF incorporating the conditional speech presence probability (SPP).

The speech distortion weighted multichannel Wiener filter (SDW-MWF) is a promising multi-microphone noise reduction technique, in particular for hearing aid applications. Its benefit over other single- and multi-microphone techniques has been shown in several previous contributions, theoretically as well as experimentally. In theoretical studies, it is usually assumed that there is a single target speech source. The filter can then be decomposed into a conceptually interesting structure, i.e., into a spatial filter (related to other known techniques) and a single-channel postfilter, which then also allows for a performance analysis. Unfortunately, it is not straightforward to make a robust practical implementation based on this decomposition. Instead, a general SDW-MWF implementation, which only requires a (relatively easy) estimation of speech and noise correlation matrices, is mostly used in practice. This paper features a theoretical study and experimental validation on a binaural hearing aid setup of this standard SDW-MWF implementation, where the effect of estimation errors in the second-order statistics is analyzed. In this case, and for a single target speech source, the standard SDW-MWF implementation is found not to behave as predicted theoretically. Second, two recently introduced alternative filters, namely the rank-one SDW-MWF and the spatial prediction SDW-MWF, are also studied in the presence of estimation errors in the second-order statistics. These filters implicitly assume a single target speech source, but still only rely on the speech and noise correlation matrices. It is proven theoretically and illustrated through experiments that these alternative SDW-MWF implementations behave close to the theoretical optimum, and hence outperform the standard SDW-MWF implementation.

Acoustic feedback is a well-known problem in hearing aids, which is caused by the undesired acoustic coupling between the loudspeaker and the microphone. Acoustic feedback limits the maximum amplification that can be used in the hearing aid without making it unstable. The goal of adaptive feedback cancellation (AFC) is to adaptively model the feedback path and estimate the feedback signal, which is then subtracted from the microphone signal. The main problem in identifying the feedback path model is the correlation between the near-end signal and the loudspeaker signal, which is caused by the closed signal loop. A possible solution to this problem is to use the prediction error method (PEM)-based AFC with a linear prediction (LP) model for the near-end signal. In this paper, a modification to the PEM-based AFC is presented where the LP model is replaced by a sinusoidal near-end signal model. More specifically, it is shown that using frequency estimation techniques to estimate the sinusoidal near-end signal model improves the performance of the PEM-based AFC compared to using a LP model. Simulation results for a hearing aid scenario indicate a significant improvement in terms of misadjustment and maximum stable gain increase.

Binaural hearing aids use microphone signals from both left and right hearing aid to generate an output signal for each ear. The microphone signals can be processed by a procedure based on speech distortion weighted multichannel Wiener filtering (SDW-MWF) to achieve significant noise reduction in a speech + noise scenario. In binaural procedures, it is also desirable to preserve binaural cues, in particular the interaural time difference (ITD) and interaural level difference (ILD), which are used to localize sounds. It has been shown in previous work that the binaural SDW-MWF procedure only preserves these binaural cues for the desired speech source, but distorts the noise binaural cues. Two extensions of the binaural SDW-MWF have therefore been proposed to improve the binaural cue preservation, namely the MWF with partial noise estimation (MWF-eta) and MWF with interaural transfer function extension (MWF-ITF). In this paper, the binaural cue preservation of these extensions is analyzed theoretically and tested based on objective performance measures. Both extensions are able to preserve binaural cues for the speech and noise sources, while still achieving significant noise reduction performance.

This paper presents combined active noise control and noise reduction schemes for hearing aids to tackle secondary path effects and effects of noise leakage through an open fitting. While such leakage contributions and the secondary acoustic path from the loudspeaker to the tympanic membrane are usually not taken into account in standard noise reduction systems, they appear to have a non-negligible impact on the final signal-to-noise ratio. Using a noise-reduction algorithm and an active noise control system in cascade may be efficient as long as the causality margin of the system is large enough. Putting the two functional blocks in parallel and then integrating them is found to lead to a more robust algorithm. A Filtered-x Multichannel Wiener Filter is presented and applied to integrate noise reduction and active noise control. The cascaded scheme and the integrated scheme are compared experimentally with a Multichannel Wiener Filter in a classic noise reduction framework without active noise control, where the integrated scheme is found to provide the best performance.

In this paper two multichannel noise reduction strategies are compared in the context of binaural hearing aids. Recently a novel noise reduction method based on spatial-temporal prediction (STP) was introduced which showed an improvement over methods based on multichannel Wiener filtering, although at the cost of a higher computational complexity. Whereas this newmethod operates in the time domain, hearing aids typically demand faster frequency domain implementations. In this paper we therefore propose a frequency domain equivalent of the STP method. The performance of the new so-called spatial prediction (SP) method will be compared to a frequency domain implementation of the speech distortion weighted multichannel Wiener filter (SDW-MWF), theoretically as well as based on simulations with a binaural hearing aid configuration. It will be shown that the frequency domain SP method still achieves some improvement over the SDW-MWF, at the cost of higher computational complexity.

In a binaural hearing aid system, output signals need to be generated for the left and the right ear. Using the binaural multichannel Wiener filter (MWF), which exploits all microphone signals from both hearing aids, a significant reduction of background noise can be achieved. However, due to power and bandwidth limitations of the binaural link, it is typically not possible to transmit all microphone signals between the hearing aids. To limit the amount of transmitted information, this paper presents reduced-bandwidth MWF-based noise reduction algorithms, where a filtered combination of the contralateral microphone signals is transmitted. A first scheme uses a signal-independent beamformer, whereas a second scheme uses the output of a monaural MWF on the contralateral microphone signals and a third scheme involves an iterative distributed MWF (DB-MWF) procedure. It is shown that in the case of a rank-1 speech correlation matrix, corresponding to a single speech source, the DB-MWF procedure converges to the binaural MWF solution. Experimental results compare the noise reduction performance of the reduced-bandwidth algorithms with respect to the benchmark binaural MWF. It is shown that the best performance of the reduced-bandwidth algorithms is obtained by the DB-MWF procedure and that the performance of the DB-MWF procedure approaches quite well the optimal performance of the binaural MWF.

This paper describes the binaural cue preservation of a noise reduction algorithm for bilateral hearing aids, namely the multichannel Wiener filter with interaural transfer function extension (MWF-ITF). An extra term is added to the cost function to preserve the binaural cues of both the speech and noise component of a signal at the cost of some noise reduction. This paper combines the theoretical analysis with objective binaural performance measures and a perceptual evaluation.

The detection of auditory steady-state responses (ASSRs) provides an objective and frequency specific technique to assess reliable hearing thresholds at audiometric frequencies. Unfortunately, the duration of ASSR measurements can be long, which is unpractical for wide scale clinical application. Therefore, we propose a multi-channel Wiener filtering (MWF) based technique with a priori knowledge through LQ factorisation as a tool to improve the ASSR detection in recorded multi-channel electroencephalogram (EEG) data obtained at intensities above hearing threshold. We conclude that this technique is able to reduce measurement duration significantly. For a multi-channel data set and implementation, near-optimal performance is obtained with five-channel recordings.

In this paper, solutions for combined feedback and noise suppression in hearing aids are developed. The techniques presented are based on the generalized sidelobe canceller (GSC) and adaptive feedback canceller (AFC), with a prediction error method (PEM) adaptation to avoid speech distortion. Two possible cascades of GSC-based noise reduction and AFC, namely an ldquoAFC firstrdquo and a ldquoGSC first,rdquo as well as a truly integrated solution that jointly suppresses feedback and noise are discussed. The integrated solution (called PEM-GFIC) achieves optimum synergies between noise and feedback suppression at the lowest computational cost. In addition, it cancels more feedback than the generalized echo and interference canceller, a joint solution for echo and noise suppression. In the cascaded solutions, the feedback and noise suppression filters are not always optimally exploited. For high input SNRs, ldquoAFC firstrdquo scheme generally may achieve better feedback cancellation because of its larger number of degrees of freedom. However, noise reduction by the GSC-stage seriously affects the feedback cancellation performance. At low SNRs, ldquoGSC firstrdquo generally achieves more feedback cancellation than PEM-GFIC at the expense of worse noise reduction. At high hearing aid gains and/or large SNRs, the noise reduction stage however negatively affects the performance of the feedback cancellation filter, resulting in a worse feedback and noise suppression compared to PEM-GFIC.

This paper presents an extension of the binaural multi-channel Wiener filtering algorithm discussed in T.J. Klasen et al. (2005). The goal of this paper is to preserve both the interaural time difference (ITD) and interaural level difference (ILD) of the speech and noise components. This is done by extending the cost function to incorporate terms for the interaural transfer functions (ITF) of the speech and noise components. Using weights, the emphasis on the preservation of the ITFs can be controlled in addition to the emphasis on noise reduction. Adapting these parameters allows one to preserve the ITFs of the speech and noise component, and therefore ITD and ILD cues, while enhancing the signal-to-noise ratio

The paper presents a binaural extension of a monaural multi-channel noise reduction algorithm for hearing aids based on Wiener filtering. The algorithm provides the hearing aid user with a binaural output. In addition to significantly suppressing the noise interference, the algorithm preserves the interaural time delay (ITD) cues of the received speech, thus allowing the user to localize the speech source correctly.

We propose an instrumental variable method for adaptive feedback cancellation (IV-AFC) in hearing aids that is based on the autoregressive modelling of the desired signal. The IV-AFC offers better feedback suppression for spectrally colored signals than the standard continuous adaptation feedback cancellers. In contrast to a previously proposed prediction error method based feedback canceller, the IV-AFC does not suffer from stability problems when the adaptive feedback canceller is highly time-varying.

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.

A real time implementation and an evaluation of a singular value decomposition (SVD) based optimal filtering technique (Doclo, S. and Moonen, M., IEEE Trans. Sig. Process., vol.50, no.9, p.2230-44, 2002) for noise reduction in a dual microphone BTE (behind-the-ear) hearing aid is presented. A method to improve the performance of a voice activity detector (VAD) is described and evaluated physically. This method is used in the real time implementation of the optimal filtering technique. A perceptual evaluation by normal hearing subjects is carried out for single and multiple jammer sound sources with speech weighted noise. The SVD-based technique can perform as well as an adaptive beamformer strategy (Maj, J.B. et al., Ear and Hearing, 2003) in a single noise scenario (i.e. the ideal scenario for the latter technique), and, can outperform the beamformer technique in a multiple noise sources scenario.

Recently, a generalized noise reduction scheme was proposed, called the spatially preprocessed speech distortion weighted multi-channel Wiener filter (SP-SDW-MWF). Compared to GSC with quadratic inequality constraint (QIC-GSC), the SP-SDW-MWF reduces more noise, for a given maximum speech distortion level. We develop time-domain and frequency-domain stochastic gradient implementations of the SP-SDW-MWF. Experimental results with a hearing aid show that the proposed stochastic gradient algorithm preserves the benefit of the SP-SDW-MWF over the QIC-GSC, while its computational cost is comparable to the NLMS based scaled projection algorithm (SPA) for QIC-GSC.

The noise reduction performance of the generalized sidelobe canceller (GSC) depends on the validity of a priori assumptions about the signal model, whereas the recently developed multi-channel Wiener filter (MWF) techniques do not, hence, their potential benefit. However, both techniques 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 MWF both theoretically and experimentally. 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 robustness constraint.