Hai Huyen Dam

Design of modified UCHT sequences

In this paper, we consider the design of a class of unified complex Hadamard transform (UCHT) sequences. An efficient modification is imposed to those sequences to better suit applications in asynchronous code-division multiple-access (CDMA) systems. These modified UCHT sequences preserve the orthogonality of the original UCHT sequences and offer increased design options due to an increased number of parameters. The design of UCHT, modified UCHT, and Oppermann sequences is then formulated with reference to optimizing the maximum nontrivial aperiodic correlation value. These optimization problems can then be solved efficiently using a genetic algorithm with the maximum nontrivial aperiodic correlation value serving in the definition of a fitness function. Numerical examples illustrate the benefits of modified UCHT sequences over the original UCHT sequences

Two-Microphone Hearing Aids Using Prediction Error Method for Adaptive Feedback Control

A challenge in hearing aids is adaptive feedback control which often uses an adaptive filter to estimate the feedback path. This estimate of the feedback path usually results in a bias due to the correlation between the loudspeaker signal and the incoming signal. The prediction error method (PEM) is a popular method for reducing this bias for adaptive feedback control (AFC) in hearing aids, providing a significant performance improvement compared to conventional adaptive feedback control techniques. However, the PEM-based AFC (PEM-AFC) applications are still limited to single-microphone single-loudspeaker (SMSL) systems. This paper investigates the application of the PEM-AFC to a two-microphone single-loudspeaker hearing aid with detailed theoretical analysis as well as practical experiments. In the proposed method, PEM-AFC2, we use the two-microphone adaptive feedback control (AFC2) method with two microphones and one loudspeaker. The incoming signals at the two microphones are related by a relative transfer function (RTF) which is used to predict the incoming signal at the main microphone. In addition, a prefilter is employed to prewhiten the loudspeaker and the microphone signals before the adaptive filter estimates. As a result, the proposed method obtains a lower bias and a faster tracking rate compared to the PEM-AFC and the AFC2 method, while still maintaining a good quality of the incoming signal. A new derivation for optimal filters in the AFC2 method will also be provided. The performance of the proposed method is evaluated for speech shaped noise as incoming signal and with undermodeling the RTF as well as with perfect modeling the RTF. Moreover, different types of incoming signals and a sudden change of feedback paths are also considered. The experimental results show that the proposed approach yields a significant performance improvement compared to existing state-of-the-art AFC methods such as the PEM-AFC and the AFC2.

Proportionate NLMS for adaptive feedback control in hearing aids

The proportionate normalized least-mean-squares (PNLMS) algorithm is commonly used in acoustic echo cancellation (AEC) context. It provides faster initial convergence and tracking rates compared to the NLMS algorithm for the case of sparse echo impulse responses. The improved PNLMS algorithm (IPNLMS) has been proven to be more powerful than PNLMS by exploiting new rules for computing the weight of each step-size corresponding to each adaptive filter coefficient. However, the application of the PNLMS and the IPNLMS algorithms for adaptive feedback control (AFC) in hearing aids (HAs) is still limited due to high correlation between the loudspeaker and incoming signals. This paper proposes implementations of the PNLMS/IPNLMS algorithms for AFC using the prediction error method (PEM) for hearing aids. The proposed methods have been evaluated for both speech and music incoming signals. Simulation shows that the proposed methods have faster initial convergence and tracking than the PEM using the NLMS algorithm (PEM-NLMS).

Improved practical variable step-size algorithm for adaptive feedback control in hearing aids

Variable step-size (VSS) schemes are popular to use in acoustic echo cancellation (AEC) contexts. However, their effective implementation in adaptive feedback cancellation (AFC) for hearing aids is still challenging due to the correlation between microphone and loudspeaker signals. We propose an improved practical VSS algorithm (IPVSS) which uses a variable step-size with upper and lower limits to control the update equation of an adaptive filter. The proposed algorithm is implemented for feedback cancellation using the prediction error method. As a result, the overall system has a fast convergence rate, a high tracking rate and a low steady-state error. The performance of the proposed approach has been evaluated for both speech and music incoming signals. The simulation results show that the proposed approach outperforms a system which only utilizes either the lower or the upper fixed step-size used in the IPVSS.

Evaluation of two-microphone acoustic feedback cancellation using uniform and non-uniform sub-bands in hearing aids

The limiting feature of hearing aids is acoustic feedback. This feedback problem has been approached by using an acoustic feedback canceller. This well known identification in a loop problem, will give a biased solution due to correlation between the desired and loudspeaker signals. Speech and music signals have long tails in the correlation. As a result, the performance of the system is considerably degraded and under certain conditions the cancellation system will be unstable. The two-microphone techniques have the potential to significantly reduce this problem. This paper introduces the applications of uniform and non-uniform sub-band techniques into the two-microphone acoustic feedback cancellation (AFC-2mics) to decorrelate input signals and individually adapt solutions in those bands. The system has been evaluated in terms of Misalignment (MisAL) and Maximum Stable Gain (MSG) using both male and female speech input signals. The simulation results show that our proposed methods provide better performance than the other existing methods.