A hearing device arrangement includes two hearing devices which are connected to each other in a data transmitting manner. Each hearing device includes an audio input unit for obtaining an input audio signal, a processing unit for audio signal processing of the input audio signal to obtain an output audio signal, a neural network which, when executed by the processing unit performs a processing step of the audio signal processing, and an audio output unit for outputting the output audio signal. The hearing device arrangement is configured to transmit neural network data of the neural network of at least one of the hearing devices to the respective other hearing device to be used in the audio signal processing by the processing unit of the respective other hearing device.

Embodiments herein disclose a method and electronic device for personalized audio enhancement. The method includes: receiving, by the electronic device, a plurality of inputs, in response to an audiogram test. The method includes generating, by the electronic device, a first audiogram representative of a first personalized audio setting to suit a first ambient context, based on the received inputs. The method also includes determining a change from the first ambient context to a second ambient context for an audio playback, analyzing a plurality of contextual parameters during the audio playback in the second ambient context, and generating a second audiogram representative of a second personalised audio setting to suit the second ambient context based on the analysis of the plurality of contextual parameters, by the electronic device.

A hearing device, e.g. a hearing aid or a headset, 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 comprises an input unit for providing at least one electric input signal in a time-frequency representation and a signal processor comprising a neural network configured to provide respective gain values G(k,t) in said time-frequency representation for reducing noise components in said at least one electric input signal. The neural network comprises at least one layer defined as a modified gated recurrent unit, termed Peak GRU, comprising memory in the form of a hidden state vector h, and wherein an output vector o is provided by said Peak GRU in dependence of an input vector x and said hidden state vector h, wherein an output o(j,t) of the Peak GRU at a given time step t is stored as said hidden state h(j,t) and used in the calculation of the output o(j,t+1) in the next time step t+1. The signal processor is configured to provide that the number of updated channels among N.sub.ch processing channels of the Peak GRU for said input vector x(t) and said hidden state vector h(t−1) at said given time instance t is limited to a number of peak values N.sub.p, where N.sub.p is smaller than N.sub.ch. A method of operating a hearing device is further disclosed.

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.

An ear-worn device is provided that operates to isolate and individually treat the received speech of a target speaker or multiple target speakers from an audio input signal detected in a multi-speaker environment. The ear-worn device uses a machine learning model that receives a voice signature of each of one or more target speakers as input signals, to identify and isolate the component of the audio input signal attributable to the target speaker(s). Once isolated, the target speaker's speech may be enhanced, de-emphasized, or otherwise processed in a manner desired by the wearer of the ear-worn device. The wearer may use an external electronic device, e.g., a phone, to select one or more target speakers in a conversation and/or configure various settings associated with processing the speech on the ear-worn device.

A hearing device, e.g. a hearing aid, is 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 hearing device comprises a) an input unit for providing at least one electric input signal in a time frequency representation k, m, where k and m are frequency and time indices, respectively, and k represents a frequency channel, the at least one electric input signal being representative of sound and comprising target signal components and noise components; and b) a signal processor comprising b1) a target signal estimator for providing an estimate of the target signal; b2) a noise estimator for providing an estimate of the noise; b3) a gain estimator for providing respective gain values in said time frequency representation in dependence of said target signal estimate and said noise estimate, wherein said gain estimator comprises a neural network, wherein the weights of the neural network have been trained with a plurality of training signals, and wherein the outputs of the neural network comprise real or complex valued gains, or separate real valued gains and real valued phases. The invention may e.g. be used in audio devices, such as hearing aids, headsets, mobile telephones, etc., operating in noisy acoustic environments.

A hearing system comprises at least one hearing aid (HA) configured to be worn by a user at or in an ear of the user, and an external, portable processing device (EPD). The at least one hearing aid comprises a) at least one HA-input transducer for providing at least one HA-electric input signal representing sound in the environment of the hearing aid; b) a configurable noise reduction system for reducing noise in the at least one HA-electric input signal or in a signal originating therefrom based on a resulting set of noise reduction parameters; c) a noise reduction controller configured to determine a local set of noise reduction parameters; and d) a data receiver configured to receive data via a communication link from the external processing device. The external processing device comprises A) at least one EPD-input transducer for providing at least one EPD-electric input signal representing sound in the environment of the external processing device; B) a parameter estimator for providing an external set of noise reduction parameters configured to reduce noise in the at least one EPD-electric input signal, or in the at least one HA-electric input signal, or in a signal originating therefrom; and C) a data transmitter configured to transmit data, including said external set of noise reduction parameters, via said communication link to the hearing aid. The noise reduction controller is configured to determine said resulting set of noise reduction parameters based on said local set of noise reduction parameters, or on said external set of noise reduction parameters, or on a mixture thereof, in dependence of a noise reduction control signal. A hearing aid and a method of operating a hearing system is further disclosed.

A method operates a hearing aid, which has a sensor, a microphone, and a receiver. Breathing difficulties of a wearer are inferred on the basis of measurement data created by the sensor and a measure for a risk is determined based thereon. An activity helping the wearer is carried out depending on the measure. Furthermore, the hearing aid is configured for carrying out the method.

Systems and Methods for assisting the hearing-impaired are described. The methods rely on obtaining audio signals from the ambient environment of a hearing-impaired person. The audio signals are analyzed by a machine learning model that can classify audio signals into audio categories (e.g. Emergency, Animal Sounds) and audio types (e.g. Ambulance Siren, Dog Barking) and notify the user leveraging a mobile or wearable device. The user can configure notification preferences and view historical logs. The machine learning classifier is periodically trained externally based on labelled audio samples. Additional system features include an audio amplification option and a speech to text option for transcribing human speech to text output.

A method is specified for identifying an interference effect. The hearing system includes a hearing device, which is worn by a user for sound output to the user. The hearing system is configured to recurrently receive a report from the user such that an interference effect is present in the sound output. If the user reports an interference effect in a present situation, multiple features of the present situation are ascertained and stored as a feature set. An identification unit compares multiple stored feature sets to one another and ascertains those features which correspond in the multiple feature sets and which are then assumed as characteristic features of the interference effect, so that the identification unit identifies the interference effect on the basis of the characteristic features.