This paper investigates the potential performance of generalized subspace filters for speech enhancement in cocktail party situations with very poor signal/noise ratio, e.g. down to -15 dB. Performance metrics output signal/noise ratio, signal/distortion ratio, speech quality rating and speech intelligibility rating are mapped as functions of two algorithm parameters, revealing clear trade-off options between noise, distortion and subjective performances and a recommended choice of trade-off. Given sufficiently good noise statistics, SNR improvements around 20 dB as well as PESQ quality and STOI intelligibility rating improvements exceeding 1.0 and 0.2 points respectively are found. This shows the potential of the method.

Enhancement of speech in non-stationary background noise is a challenging task, and conventional single channel speech enhancement algorithms have not been able to improve the speech intelligibility in such scenarios. The work proposed in this paper investigates a single channel Kalman filter based speech enhancement algorithm, whose parameters are estimated using a codebook based approach. The results indicate that the enhancement algorithm is able to improve the speech intelligibility and quality according to objective measures. Moreover, we investigate the effects of utilizing a speaker specific trained codebook over a generic speech codebook in relation to the performance of the speech enhancement system.

Recently, the ideal binary mask has been introduced in the modulation domain by extending the ideal channel selection method to modulation channel selection [1]. This new method shows substantial improvement in speech intelligibility but less than its predecessor despite the higher complexity. Here, we extend the previous finding from [1] and provide a more direct comparison of binary masking in the modulation domain with binary masking in the time-frequency domain. Subjective and objective evaluations are performed and provide additional insight into modulation domain processing.

Feedback oscillation is one of the major issues with hearing aids. An effective way of feedback suppression is adaptive feedback cancellation, which uses an adaptive filter to estimate the feedback path. However, when the external input signal is correlated with the receiver input signal, the estimate of the feedback path is biased. This so-called “bias problem” results in a large modeling error and a cancellation of the desired signal. This paper proposes a band-limited linear predictive coding based approach to reduce the bias. The idea is to replace the hearing-aid output with a synthesized signal, which sounds perceptually the same as or similar to the original signal but is statistically uncorrelated with the external input signal at high frequencies where feedback oscillation usually occurs. Simulation results show that the proposed algorithm can effectively reduce the bias and the misalignment between the real and the estimated feedback path. When combined with filtered-X adaptation in the feedback canceller, this approach reduces the misalignment even further.

This paper proposes a reflection model for the dynamic feedback path of digital hearing aids and compares it with two existing models: a direct model and an initialization model, based on the measured dynamic feedback paths. The comparison shows that the proposed model is superior to the existing two models in terms of maximum stable gain (MSG). For hearing aids with dual microphones, the possibility of relating the two dynamic feedback paths is also investigated. It is shown that in a complicated acoustic environment, the relation between the two feedback paths can be very intricate and difficult to exploit in modelling the dynamic feedback paths.

This paper investigates the use of data-adaptive spectral estimation techniques for blood velocity estimation in medical ultrasound. Current commercial systems are based on the averaged periodogram, which requires a large observation window to give sufficient spectral resolution. Herein, we propose a novel data-adaptive method to form the blood velocity spectral estimate. The method is evaluated using realistic field II simulations for both steady and unsteady flow. The latter representing the femoral artery with strong tissue interference. The method is compared to the averaged periodogram and a Capon-based estimator. The simulations indicate that the proposed method offer a significant performance gain, suggesting that the frame-rate may be increased dramatically by using adaptive spectral estimation techniques.