A sound producing device includes a first sound producing cell, driven by a first driving signal and configured to produce a first acoustic sound on a first audio band, and a second sound producing cell, driven by a second driving signal and configured to produce a second acoustic sound on a second audio band different from the first audio band. A first membrane of the first sound producing cell and a second membrane of the second sound producing cell are Micro Electro Mechanical System fabricated membranes. The first audio band is upper bounded by a first maximum frequency; the second audio band is upper bounded by a second maximum frequency. A first resonance frequency of the first membrane is higher than the first maximum frequency of the first driving signal. A second resonance frequency of the second membrane is higher than the second maximum frequency of the second driving signal.

A control unit for a worker wearing personal protective equipment is presented. The control unit includes a PPE network creator that, when activated, creates a network. The control unit also includes a PPE device identifier configured to detect a first PPE associated with the worker and a second PPE associated with the worker. The first PPE has a first speaker and a first microphone. The second PPE has a second speaker and a second microphone. The control unit also comprises a PPE network joiner that facilitates the first and second PPE joining the network. The control unit also comprises a preferred configuration selector that automatically selects a preferred speaker for the worker and a preferred microphone for the worker. The preferred speaker is the first or second speaker. The preferred microphone is the first or second microphone. The control unit also comprises a network communication component that automatically communicates a first device parameter settings to the first PPE and a second device parameter settings to the second PPE. The first and second device parameter settings are based on the selected preferred speaker and microphone.

Examples of system for ambient aversive sound detection, identification and management are described. The system comprises an earpiece device with a microphone configured to capture ambient sound around a user and sample it into small segments of the ambient sound, a speaker and a regulator to regulate the ambient sound segment transmitted to the speaker. The system further comprises a processing unit that identifies aversive ambient sound signals in the captured sound segment and provide recommendation action to manage the aversive sound signal by removing, supressing, attenuating or masking the aversive signals.

A headset, comprising: first and second ear cups, and a power and signal connector that extends between the ear cups, wherein each ear cup comprises a housing having a mounting structure for mounting the ear cup on a wearable support; a speaker; and a speaker drive circuit for operating the speaker. The first ear cup further comprises: an external power and signal connector; and a mounting structure having a first profile for mounting a removable control module having a corresponding first profile on the first ear cup such that corresponding power and signal connectors can engage the external power and signal connector so that the removable control module can provide operating signals to the speaker drive circuits. The second ear cup comprises an external power connector; and a mounting structure having a second profile for mounting a removable battery pack having a corresponding second profile on the second ear cup such that a corresponding power connector on the battery pack can engage the external power connector so that the removable battery pack can provide power to the control module via the power and signal connector.

A method comprises obtaining ear modeling data, wherein the ear modeling data includes a 3D model of an ear canal; applying a shell generation to generate a shell shape based on the ear modeling data, wherein the shell-generation model is a machine learning model and the shell shape is a 3D representation of a shell of an ear-wearable device; applying a set of one or more component-placement models to determine, based on the ear modeling data, a position and orientation of a component of the ear-wearable device, wherein the component-placement models are independent of the shell-generation model and each of the component-placement models is a separate machine learning model; and generating an ear-wearable device model based on the shell shape and the 3D arrangement of the components of the ear-wearable device.

A method comprises obtaining ear modeling data, wherein the ear modeling data includes a 3D model of an ear canal; applying a shell generation to generate a shell shape based on the ear modeling data, wherein the shell-generation model is a machine learning model and the shell shape is a 3D representation of a shell of an ear-wearable device; applying a set of one or more component-placement models to determine, based on the ear modeling data, a position and orientation of a component of the ear-wearable device, wherein the component-placement models are independent of the shell-generation model and each of the component-placement models is a separate machine learning model; and generating an ear-wearable device model based on the shell shape and the 3D arrangement of the components of the ear-wearable device.

A system and method for monitoring user sound exposure are disclosed. The system can include receiving audio data generated by an audio source, such as a mobile phone music player, and ambient sound data. Based on this received sound data, the system can determine a cumulative sound exposure for the user, compare the cumulative sound exposure to a threshold, and provide an alert to the user according to the comparison. Based on whether the user adheres to recommended safe listening standards, the system can take additional actions, including providing reports to the user or automatically controlling the volume of the sound that the user is exposed to.

The invention relates to a system comprising a hearing protection device (13) configured to be worn by a user (10); one or more audio output devices (26) configured to generate one or more audio signals, a computing device (60) comprising a memory and one or more computer processors, wherein the memory comprises instructions that when executed by the one or more computer processors cause the one or more computer processor to: —select a training configuration (25) that defines a set of audio events (74A) that correspond to a set of user reactions (74B), —send a set of control signals to the one or more audio output device (26) that cause the one or more audio output devices (26) to simulate the set of audio events (74A), —receive reaction data (74C) that indicates whether the user (10) provided the set of user reactions (74B) to the set of audio events (74A); and —perform at least one operation based at least in part on whether the user (10) provided the set of user reactions (74B) to the set of audio events (74A) while wearing the hearing protection device (13).

According to examples, an apparatus may include a chamber having a hole and a microphone unit. The microphone unit may include a first substrate having a first opening aligned with the hole of the chamber, a second substrate positioned with respect to the first substrate to form a gap between the second substrate and the first substrate, the second substrate having a second opening, and a diaphragm housed within the gap formed between the first substrate and the second substrate, in which the first opening is positioned on a first side of the diaphragm and the second opening is positioned on a second side of the diaphragm.

An audio system has an ambient sound enhancement function, in which an against-the-ear audio device having a speaker converts a digitally processed version of an input audio signal into sound. The audio system also has an acoustic noise cancellation (ANC) function that may be combined in various ways with the sound enhancement function, and that may be responsive to voice activity detection. Other aspects are also described and claimed.