A hearing aid comprises at least one input unit for providing at least one stream of samples of an electric input signal in a first domain; at least one encoder configured to convert said at least one stream of samples of the electric input signal in the first domain to at least one stream of samples of the electric input signal in a second domain; a processing unit configured to process said at least one electric input signal in the second domain, to provide a compensation for the user's hearing impairment, and to provide a processed signal as a stream of samples in the second domain; a decoder configured to convert said stream of samples of the processed signal in the second domain to a stream of samples of the processed signal in the first domain. The at least one encoder is configured to convert a first number of samples from said at least one stream of samples of the electric input signal in the first domain to a second number of samples in said at least one stream of samples of the electric input signal in the second domain. The decoder is configured to convert said second number of samples from said stream of samples of the processed signal in the second domain to said first number of samples in said stream of samples of the electric input signal in the first domain. The second number of samples is larger than the first number of samples. The at least one encoder is trained, and at least a part of said processing unit providing said compensation for the user's hearing impairment is implemented as a trained neural network. A method of operating a hearing aid is further disclosed. Thereby an improved hearing aid may be provided.

A method for fitting a hearing instrument comprises obtaining sensor data from a plurality of sensors belonging to a plurality of sensor types; applying a machine learned (ML) model to determine, based on the sensor data, an applicable fitting category of the hearing instrument from among a plurality of predefined fitting categories, wherein the plurality of predefined fitting categories includes a fitting category corresponding to a correct way of wearing the hearing instrument and a fitting category corresponding to an incorrect way of wearing the hearing instrument; and generating an indication based on the applicable fitting category of the hearing instrument.

A hearing device comprises an input transducer comprising a microphone for providing an electric input signal representative of sound in the environment of the hearing device, a pre-processor for processing electric input signal and providing a multitude of feature vectors, each being representative of a time segment thereof, a neural network processor adapted to implement a neural network for implementing a detector configured to provide an output indicative of a characteristic property of the at least one electric input signal, the neural network being configured to receive said multitude of feature vectors as input vectors and to provide corresponding output vectors representative of said output of said detector in dependence of said input vectors. The hearing device further comprises a transceiver comprising a transmitter and a receiver for establishing a communication link to another part or device or server, at least in a particular adaptation-mode of operation, and a selector for—in said particular adaptation—mode of operation—routing said feature vectors to said transmitter for transmission to said another part or device or server, and—in a normal mode of operation—to route said feature vectors to said neural network processor for use as inputs to said neural network, a neural network controller connected to said neural network processor for—in said particular adaptation-mode of operation—receiving optimized node parameters, and to apply said optimized node parameters to said nodes of said neural network to thereby implement an optimized neural network in said neural network processor, wherein the optimized node parameters have been selected among a multitude of sets of node parameters for respective candidate neural networks according to a predefined criterion in dependence of said feature vectors. A method of selecting optimized parameters for a neural network for use in a portable hearing device is further disclosed. The invention may e.g. be used in hearing aids or headsets, or similar, e.g. wearable, devices.

A hearing device directly or indirectly connected to a server through a network, the hearing device being provided with: an input unit that acquires sound data from the outside; a communication unit that transmits the sound data to the server, and receives a parameter set generated on the basis of the analysis result obtained by analyzing the sound data in the server; a storage unit that stores the parameter set; and an adjustment unit that, on the basis of the parameter set, adjusts gains of a plurality of prescribed frequencies.

An ear-worn electronic device comprises at least one microphone configured to sense sound in an acoustic environment, an acoustic transducer, and a non-volatile memory configured to store a plurality of parameter value sets, each of the parameter value sets associated with a different acoustic environment. A control input is configured to receive a control input signal produced by at least one of a user-actuatable control of the ear-worn electronic device and an external electronic device communicatively coupled to the ear-worn electronic device in response to a user action. A processor is operably coupled to the microphone, the acoustic transducer, the non-volatile memory, and the control input. The processor is configured to classify the acoustic environment using the sensed sound and apply, in response to the control input signal, one of the parameter value sets appropriate for the classification.

Systems and methods for improved control and performance of cochlear implants are disclosed. In an embodiment, the audio environment is sampled, and a neural network determines suggested filter setting for the cochlear implant. The process is repeated such that, as the user moves through various audio environments having differing noise levels, satisfactory performance of the cochlear implant is maintained for the user.

A hearing device, e.g. a hearing aid, comprises a) a multitude of input units, each providing an electric input signal representing sound in the environment of the user in a time-frequency representation, wherein the sound is a mixture of speech and additive noise or other distortions, e.g. reverberation, b) a multitude of beamformer filtering units, each being configured to receive at least two, e.g. all, of said multitude of electric input signals, each of said multitude of beamformer filtering units being configured to provide a beamformed signal representative of the sound in a different one of a multitude of spatial segments, e.g. spatial cells, around the user, c) a multitude of speech probability estimators each configured to receive the beamformed signal for a particular spatial segment and to estimate a probability that said particular spatial segment contains speech at a given point in time and frequency, wherein at least one, e.g. all, of the multitude of speech probability estimators is/are implemented as a trained neural network, e.g. a deep neural network. The invention may e.g. be used in hearing aids or communication devices, such as headsets, or telephones, or speaker phones.

An audio system for user hearing assessment includes one or more audio capture devices, and processing circuitry. The one or more audio capture devices are configured to capture audio of a conversation of a user and convert the audio to audio signals. The processing circuitry is configured to use the audio signals to identify multiple conditions associated with user hearing difficulty. The conditions include any of words, phrases, frequencies, or phonemes, and environmental audio conditions that are followed by an indication of user hearing difficulty. The processing circuitry is configured to generate a hearing profile for the user based on the identified conditions associated with user hearing difficulty. The processing circuitry is configured to adjust an operation of an audio output device using the hearing profile to reduce a frequency of user hearing difficulty if the user requires audio enhancement.

A computing system comprising one or more electronic computing devices receives data from a hearing-assistance device. The computing system determines, based on the data received from the hearing-assistance device, a cognitive benefit measure for a wearer of the hearing-assistance device. The cognitive benefit measure being an indication of a change of a cognitive benefit of the wearer of the hearing-assistance device attributable to use of the hearing-assistance device by the wearer of the hearing-assistance device. The computing device outputs an indication of the cognitive benefit measure.

Disclosed herein, among other things, are apparatus and methods for neural network-driven feedback cancellation for hearing assistance devices. Various embodiments include a method of signal processing an input signal in a hearing assistance device to mitigate entrainment, the hearing assistance device including a receiver and a microphone. The method includes performing neural network processing to identify acoustic features in a plurality of audio signals and predict target outputs for the plurality of audio signals, and using the trained neural network to control acoustic feedback cancellation of the input signal.