The present disclosure provides a microphone, comprising a shell structure, a vibration pickup assembly, a vibration pickup assembly, wherein the vibration pickup assembly is accommodated in the shell structure and generates vibration in response to an external sound signal transmitted to the shell structure, and at least two acoustoelectric conversion elements configured to respectively receive the vibration of the vibration pickup assembly to generate an electrical signal, wherein the at least two acoustoelectric conversion elements have different frequency responses to the vibration of the vibration pickup assembly.

The present disclosure provides a microphone, comprising a shell structure, a vibration pickup assembly, a vibration pickup assembly, wherein the vibration pickup assembly is accommodated in the shell structure and generates vibration in response to an external sound signal transmitted to the shell structure, and at least two acoustoelectric conversion elements configured to respectively receive the vibration of the vibration pickup assembly to generate an electrical signal, wherein the at least two acoustoelectric conversion elements have different frequency responses to the vibration of the vibration pickup assembly.

An attachment device for holding an auscultation device near a smart device. The attachment device includes an interior compartment for receiving the smart device and a port for receiving the auscultation device. In use, the attachment device holds the microphone of the smart device adjacent to the auscultation device. The smart device records the heart or breathing sounds from the auscultation device, and a mobile application on the smart device classifies the sounds as normal or abnormal.

An attachment device for holding an auscultation device near a smart device. The attachment device includes an interior compartment for receiving the smart device and a port for receiving the auscultation device. In use, the attachment device holds the microphone of the smart device adjacent to the auscultation device. The smart device records the heart or breathing sounds from the auscultation device, and a mobile application on the smart device classifies the sounds as normal or abnormal.

The disclosure is directed to methods for transmitting vibrations via an electronic and/or transducer assembly through a user or wearer's tooth, teeth, jaw, and/or other bones. The disclosure is further directed to a system for transmitting vibrations via an electronic and/or transducer assembly through a user or wearer's tooth, teeth, jaw and/or other bones. The disclosure is further directed to an oral apparatus for implementation of methods and systems described herein.

A material for an acoustic matching layer contains the following components (A), (B), and (C): (A) an epoxy resin; (B) a curing agent; and (C) surface-treated tungsten carbide particles subjected to a surface treatment with a surface treatment agent including at least one of an aminosilane compound, a mercaptosilane compound, an isocyanatosilane compound, a thiocyanatosilane compound, an aluminum alkoxide compound, a zirconium alkoxide compound, or a titanium alkoxide compound.

Voice command recognition and natural language recognition are carried out using an accelerometer that senses signals from the vibrations of one or more bones of a user and receives no audio input. Since word recognition is made possible using solely the signal from the accelerometer from a person's bone conduction as they speak, an acoustic microphone is not needed and thus not used to collect data for word recognition. According to one embodiment, a housing contains an accelerometer and a processor, both within the same housing. The accelerometer is preferably a MEMS accelerometer which is capable of sensing the vibrations that are present in the bone of a user as the user is speaking words. A machine learning algorithm is applied to the collected data to correctly recognize words spoken by a person with significant difficulties in creating audible language.

Voice command recognition and natural language recognition are carried out using an accelerometer that senses signals from the vibrations of one or more bones of a user and receives no audio input. Since word recognition is made possible using solely the signal from the accelerometer from a person's bone conduction as they speak, an acoustic microphone is not needed and thus not used to collect data for word recognition. According to one embodiment, a housing contains an accelerometer and a processor, both within the same housing. The accelerometer is preferably a MEMS accelerometer which is capable of sensing the vibrations that are present in the bone of a user as the user is speaking words. A machine learning algorithm is applied to the collected data to correctly recognize words spoken by a person with significant difficulties in creating audible language.

An apparatus for recording sound of an instrument is disclosed. The apparatus has a contact microphone configured to contact the instrument, and an ambient microphone. The ambient microphone is configured to record ambient sound at a location of the instrument as a first signal or data. The contact microphone is insensitive to air vibrations and is configured to record vibrations of the instrument as a second signal or data.

An apparatus for recording sound of an instrument is disclosed. The apparatus has a contact microphone configured to contact the instrument, and an ambient microphone. The ambient microphone is configured to record ambient sound at a location of the instrument as a first signal or data. The contact microphone is insensitive to air vibrations and is configured to record vibrations of the instrument as a second signal or data.