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

2025-05-15

AI Summary

This work addresses spatially selective active noise control (ANC) for open-fit hearing aids—i.e., suppressing interfering noise while preserving target-direction speech. We propose a novel ANC framework based on non-causal relative impulse response (RIR) modeling. To our knowledge, this is the first application of non-causal filters in spatially selective ANC optimization, relaxing conventional causality constraints. The framework integrates spatial acoustic field modeling, RIR estimation, and multi-objective optimization—jointly minimizing speech distortion, maximizing noise suppression, and enhancing signal-to-noise ratio (SNR). Simulation results demonstrate that, compared to causal baseline methods, the proposed approach achieves 4.2 dB greater noise attenuation, 38% lower speech distortion, and consistent SNR improvement across varying delays and degrees of non-causality. It significantly enhances directional selectivity and speech fidelity, establishing a new paradigm for open-fit hearing aid design.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Non-convex Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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.

Problem

• Improving spatially selective noise control in hearables
• Incorporating acausal optimization for better performance
• Reducing noise while preserving desired directional sound

Innovation

• Acausal optimization for noise control
• Spatially selective sound suppression
• Open-fitting hearables performance improvement