This paper investigates the impact of input feature frequency resolution and hyperparameter choices on deep neural network based binaural speech enhancement (SE) for hearing aids. We analyze how time-frequency resolution parameters, such as window length and hop ratio, influence speech intelligibility and computational efficiency. Performance metrics, including Modified Binaural Short-Time Objective Intelligibility (MBSTOI) and Signal-to-Noise Ratio improvement (SNRi) are used to evaluate model efficacy. Our findings show that higher frequency resolutions improve intelligibility but demand greater computational resources, while optimized window and hop settings strike a balance between performance and complexity. Additionally, we use Pareto curves to analyze trade-offs between model complexity and performance, offering guidelines for designing efficient audio pipelines for resource-constrained devices, paving the way for practical binaural SE deployment in hearing aids.

This paper presents an efficient implementation of a robust adaptive beamforming algorithm based on convex optimization for applications in the processing-constrained environment of a digital hearing aid. Several modifications of the standard interior point barrier method are introduced for use where the array data covariance matrix is changing rapidly relative to the algorithm's convergence rate. These efficiency improvements significantly simplify the computation without affecting the algorithm's fast convergence, and are useful for real-time adaptive beamforming regardless of the rate of array correlation change. Simulation results show that this implementation is numerically stable and succeeds where many minimum-variance distortionless response (MVDR) solutions fail.

We describe a system for integrating a genetic algorithm (GA) with perceptual feedback to perform an efficient search in a perceptual space. The main system components are an efficient method for estimating perceptual rank order and genetic operators that take advantage of the types of parameters found in certain classes of audio processing systems. Preference judgments are used, resulting in a lightweight user interface. The application to subjectively fitting a portable hearing aid based solely on binary feedback is discussed. An experiment was conducted using eight normal and eight hearing impaired subjects. Three parameters were varied to control cancellation of acoustic feedback. The GA worked well for fitting this system, as indicated by both objective and subjective measures. In addition, users had greatly differing preferences for feedback cancellation parameters and these preferences did not change much when subjects were retested.

We demonstrate that a genetic algorithm (GA) can efficiently generate accurate low-order pole-zero approximations of head-related transfer functions (HRTFs) from measured impulse responses by minimizing a logarithmic error criterion. This approach is much simpler and comparable or superior in efficiency to competing search algorithms. We build on previous work in low-order HRTF approximation. By applying the GA, we converge to solutions of equal quality in about 30 s compared to over 20 min. This work develops a basic steady-state GA using a pole-zero filter design problem as an illustrative example. We propose a domain-appropriate error measure. We then apply the algorithm to designing filters to approximate measured HRTFs. Detailed performance measurements are presented. In the appendix, we propose a widely applicable population variation metric. A lower bound is developed for this metric and is used to detect convergence.