cs.SDSep 18, 2026

Causal limits and optimal allocation for passive frequency-selective hearing protection

Authors: Geonhwi Hwang, Jaewoo Lee, Hyunjun Kim, Hyeonwoo Na

Organizations: Department of Applied Physics, Hokkaido University, Sapporo 060-8628, Japan · CHA University School of Medicine, Gyeonggi-do 13488, Republic of Korea · School of Computing, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea · Division of Mechanical and Space Engineering, Hokkaido University, Sapporo 060-8628, Japan

Abstract

Passive Helmholtz-resonator earplugs aim to attenuate hazardous noise while keeping speech audible; the air volume of an ear-sized shell is finite, and we treat it as the resource a design spends. For a vented bore transparent at zero frequency and loaded by reactive side branches, causality fixes a conserved integral of transmission loss. We derive it in device variables, add viscous and flanking constraints, and allocate the budget by hazard-weighted water-filling with a continuous speech-cost price. The audio-band budget B_ad = (20/ln10) pi^2 V / 2cS is set by the ratio of total cavity volume V to bore area S; occluding plugs are exempt. The f^2 price signal favours frequencies well above the source peak. Replacing strict transparency by a speech-cost price gives an exchange rate of about 1.3 dB of protection per decibel of speech cost under the present model, with the budget setting where it saturates. Across three form factors, low-frequency-selective designs stay near a decibel while hazard-matched mid/high-frequency designs reach twelve. The binding variables are bore area and speech allowance, not resonator count.

Figures & tables

Explore similar work

CardsList
  1. ABSE-NET: A Lightweight Neural Model for Active Binaural Speech Enhancement in Open-Fit Hearing Aids

    Sep 1, 2026De Hu, Xue Du, Qingying Zhao +1Speech Enhancement

  2. CAPS: A Cascaded Reconstruction Model to Power Saving in Hearables Using Sub-Nyquist Sampling with Bandwidth Extension

    Jul 21, 2026Tarikul Islam Tamiti, Sajid Fardin Dipto, Luke Baja-Ricketts +2Energy-Efficient MLSpeech Enhancement

  3. End-to-End Multi-Task Learning for Adjustable Joint Noise Reduction and Hearing Loss Compensation

    Mar 20, 2026Philippe Gonzalez, Vera Margrethe Frederiksen, Torsten Dau +1Speech EnhancementMulti-Task Learning