Spatially Selective Active Noise Control for Open-fitting Hearables with Acausal Optimization

TL;DR

This paper introduces an acausal optimization method for spatially selective active noise control in hearables, significantly enhancing noise suppression and speech preservation compared to causal designs.

Contribution

The work presents a novel acausal optimization approach that improves spatial noise control performance in hearables by better modeling source responses.

Findings

Acausal filters outperform causal ones in noise reduction.

Performance gains are consistent across different delays.

Acausal approach better preserves desired speech signals.

Abstract

Recent advances in active noise control have enabled the development of hearables with spatial selectivity, which actively suppress undesired noise while preserving desired sound from specific directions. In this work, we propose an improved approach to spatially selective active noise control that incorporates acausal relative impulse responses into the optimization process, resulting in significantly improved performance over the causal design. We evaluate the system through simulations using a pair of open-fitting hearables with spatially localized speech and noise sources in an anechoic environment. Performance is evaluated in terms of speech distortion, noise reduction, and signal-to-noise ratio improvement across different delays and degrees of acausality. Results show that the proposed acausal optimization consistently outperforms the causal approach across all metrics and scenarios, as acausal filters more effectively characterize the response of the desired source.