Sound Pressure Minimization at the Ear Drum for In-ear ANC Headphones using a Fixed Feedforward Remote Microphone Technique

TL;DR

This paper presents a fixed feedforward ANC controller for in-ear headphones that minimizes sound pressure at the eardrum by predicting acoustic paths, optimizing FIR filters, and ensuring stability, with simulations demonstrating effective attenuation across various sound directions.

Contribution

It introduces a novel fixed feedforward ANC method using measured acoustic paths and stability constraints, optimized for multiple sound fields and subject variations.

Findings

Achieves at least -10 dB attenuation over a wide range of directions.

Controller optimized for diffuse fields provides broader attenuation.

Optimization for multiple measurements improves robustness against variations.

Abstract

In this paper we consider an in-ear headphone equipped with an external microphone and aim to minimize the sound pressure at the ear drum by means of a fixed feedforward ANC controller. Based on measured acoustic paths to predict the sound pressure generated by external sources and the headphone at the ear drum, the FIR filter coefficients of the ANC controller are optimized for different sound fields. Due to the acoustic feedback path between the loudspeaker and the microphone, a stability constraint based on the Nyquist stability criterion is introduced. Performance degradations due to reinsertions of the headphone and intra-subject variations are addressed by simultaneously optimizing the controller for several measurement repetitions of the acoustic paths. Simulations show that the controller optimized for an ipsilateral excitation produces an attenuation of at least -10 dB that extends approximately to +45{\deg} and -65{\deg} from the ipsilateral DoA. The controller optimized for a diffuse-field excitation achieves an attenuation of at least -10 dB over a wider range of DoAs on the ipsilateral side, namely +90{\deg} to -90{\deg}. Optimizing the controller for several measurement repetitions is shown to be effective against performance degradations due to reinsertions and intra-subject variations.