Method for protected noise exposure level assessment under an in-ear hearing protection device: a pilot study.

OBJECTIVE: To properly measure the effective noise exposure level of workers with hearing protection devices (HPD), the use of in-ear noise dosimeters (IEND) is increasing. Commercial IENDs typically feature one in-ear microphone that captures all noises inside the ear and do not discriminate the residual noise in the earcanal from wearer-induced disturbances (WID) to calculate the in-ear sound pressure levels (SPL). A method to alleviate this particular issue with IENDs and calculate the hearing protection level on-site is therefore proposed. DESIGN: The sound captured by an outer-ear microphone is filtered with the modelled HPD transfer function to estimate the in-ear SPL, this way part of the WIDs mostly captured by the in-ear microphone can be rejected from the SPL. The level of protection provided by the earplugs can then be estimated from the difference between in-ear and outer-ear SPLs. The proposed method is validated by comparing the outcome of the proposed WID rejection method to a reference method. STUDY SAMPLE: The detailed methods are assessed on audio recordings from 16 industrial workers monitored for up to 4 days. RESULTS: The merits of the proposed WID rejection approach are discussed in terms of residual SPL and hearing protection level estimation accuracy. CONCLUSIONS: Based on the findings, a method to integrate the proposed WID rejection algorithm in future IENDs is suggested.

An in-ear speech database in varying conditions of the audio-phonation loop.

With the rise of hearables and the advantages of using in-ear microphones with intra-aural devices, accessibility to an in-ear speech database in adverse conditions is essential. Speech captured inside the occluded ear is limited in its frequency bandwidth and has an amplified low frequency content. In addition, occluding the ear canal affects speech production, especially in noisy environments. These changes to speech production have a detrimental effect on speech-based algorithms. Yet, to the authors' knowledge, there are no speech databases that account for these changes. This paper presents a speech-in-ear database, of speech captured inside an occluded ear in noise and in quiet. The database is bilingual (in French and in English) and is intended to aid researchers in developing algorithms for intra-aural devices utilizing in-ear microphones.