A hearing system configured to be located at or in the head of a user, comprises a) at least two microphones providing at least two electric input signals, b) an own voice detector, c) access to a database (O.sub.l, H.sub.l) comprising c1) relative or absolute own voice transfer function(s), and corresponding c2) absolute or relative acoustic transfer functions for a multitude of test-persons, d) a processor connectable to the at least two microphones, to the own voice detector, and to the database. The processor is configured A) to estimate an own voice relative transfer function for sound from the user's mouth to at least one of the at least two microphones, and B) to estimate personalized relative or absolute head related acoustic transfer functions from at least one spatial location other than the user's mouth to at least one of the microphones of the hearing system in dependence of the estimated own voice relative transfer function(s) and the database (O.sub.l, H.sub.l). The hearing system further comprises e) a beamformer configured to receive the at least two electric input signals, or processed versions thereof, and to determine personalized beamformer weights based on the personalized relative or absolute head related acoustic transfer functions or impulse responses. A method of determining personalized beamformer coefficients (w.sub.k) is further disclosed.

A hearing aid comprises a) at least one input transducer for providing at least one electric input signal representing said sound; b) an output transducer for providing stimuli perceivable to the user as sound; c) a feedback control system configured to minimize feedback from said output transducer to said at least one input transducer, and to at least provide a feedback corrected version of said at least one electric input signal; and d) an audio signal processor configured to apply one or more processing algorithms to said feedback corrected version of said at least one electric input signal, and to provide a processed signal in dependence thereof. The feedback control system is based on a machine learning model receiving input data at least representing said at least one electric input signal; and said processed signal; and providing said feedback corrected version of the at least one electric input signal as an output. A method of training a machine learning model is further disclosed.

An audio scene classifier classifies an audio input signal from an audio scene and includes a pre-processing neural network configured for pre-processing the audio input signal based on initial classification parameters to produce an initial signal classification, and a scene classifier neural network configured for processing the initial scene classification based on scene classification parameters to produce an audio scene classification output. The initial classification parameters reflect neural network training based on a first set of initial audio training data, and the scene classification parameters reflect neural network training on a second set of classification audio training data separate and different from the first set of initial audio training data. A hearing implant signal processor configured for processing the audio input signal and the audio scene classification output to generate the stimulation signals to the hearing implant for perception by the patient as sound.

The disclosure generally relates to a method, system and apparatus to improve a user's understanding of speech in real-time conversations by processing the audio through a neural network contained in a hearing device. The hearing device may be a headphone or hearing aid. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; a neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and a digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

The disclosure generally relates to a method, system and apparatus to improve a user's understanding of speech in real-time conversations by processing the audio through a neural network contained in a hearing device. The hearing device may be a headphone or hearing aid. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; a neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and a digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

The disclosure generally relates to a method, system and apparatus to improve a user's understanding of speech in real-time conversations by processing the audio through a neural network contained in a hearing device. The hearing device may be a headphone or hearing aid. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; a neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and a digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

The disclosure generally relates to a method, system and apparatus to improve a user's understanding of speech in real-time conversations by processing the audio through a neural network contained in a hearing device. The hearing device may be a headphone or hearing aid. In one embodiment, the disclosure relates to an apparatus to enhance incoming audio signal. The apparatus includes a controller to receive an incoming signal and provide a controller output signal; a neural network engine (NNE) circuitry in communication with the controller, the NNE circuitry activatable by the controller, the NNE circuitry configured to generate an NNE output signal from the controller output signal; and a digital signal processing (DSP) circuitry to receive one or more of controller output signal or the NNE circuitry output signal to thereby generate a processed signal; wherein the controller determines a processing path of the controller output signal through one of the DSP or the NNE circuitries as a function of one or more of predefined parameters, incoming signal characteristics and NNE circuitry feedback.

Disclosed herein, among other things, are systems and methods for a user adjustment interface using remote computing resources. Specifically, a system can include a mobile device in communication with a hearing assistance device or a remote server. The mobile device can interpret an acoustic environment and send information about the environment to a remote server. The remote server can determine and send information to the mobile device for use in a user interface. The mobile device can receive a user selection of hearing assistance parameter information to be sent to the hearing assistance device.

A method of defining and setting a nonlinear signal processing of a hearing device, e.g. a hearing aid, by machine learning is provided. The hearing device being configured to be worn by a user at or in an ear or to be fully or partially implanted in the head at an ear of the user, the method comprising providing at least one electric input signal representing at least one input sound signal from an environment of a hearing device user, determining a normal-hearing representation of said at least one electric input signal based on a normal-hearing auditory model, determining a hearing-impaired representation of said at least one electric input signal based on a hearing-impaired auditory model, determining optimised training parameters by machine learning, where determining optimised training parameters comprises iteratively adjusting the training parameters, and comparing the normal-hearing representation with the hearing-impaired representation to determine a degree of matching between the normal-hearing representation and the hearing-impaired representation, until the degree of matching fulfils predetermined requirements, and, when the degree of matching fulfils the predetermined requirements, determining corresponding signal processing parameters of the hearing device based on the optimised training parameters. A hearing device is further provided.

A hearing aid system comprising at least a first hearing aid, wherein the first hearing aid is configured to establish a communication link over the internet with a remote entity based on a protocol stack, wherein the protocol stack includes an internet protocol, and the protocol stack is implemented in the first hearing aid.