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EP-4742700-A2 - METHOD FOR OPERATING A HEARING AID

EP4742700A2EP 4742700 A2EP4742700 A2EP 4742700A2EP-4742700-A2

Abstract

The invention relates to a method (28) for operating a hearing aid (2) comprising a neural network (18) with several neurons (22), each of which is assigned a weighting vector (40) with binary weights (44). Each neuron (22) is supplied with an input vector (34) with binary values (36) and processed with the weighting vector (40) to obtain a transfer function (42). The transfer function (42) is processed with an activation function (48) such that a binary result (26) is provided. The invention further relates to a method (52) for training a neural network (18) and a hearing aid (2).

Inventors

  • Müller, Michael

Assignees

  • Sivantos Pte. Ltd.

Dates

Publication Date
20260513
Application Date
20250602

Claims (8)

  1. Method (28) for operating a hearing aid (2) comprising a neural network (18) with several neurons (22), each of which is assigned a weighting vector (40) with binary weights (44), wherein each neuron (22) is supplied with an input vector (34) with binary values (36) and processed with the weighting vector (40) to obtain a transfer function (42), and wherein the transfer function (42) is processed with an activation function (48) such that a binary result (26) is provided.
  2. Method (28) according to claim 1, characterized by that the transfer function (42) is the sum of the XNOR operation on all values (36) with the respective assigned weight (44).
  3. Method (28) according to claim 2, characterized by that the sign of the transfer function (42) is used as the activation function (46).
  4. Method (28) according to any one of claims 1 to 3, characterized by that the input vector (34) is created from captured audio signals (18), and/or that a forecast (20) for future audio signals is created from the results (26).
  5. Method (52) for training a neural network (18) according to one of claims 1 to 4, wherein at each training step - the weights (44) are binarized, - the weights (44) are normalized, for which they are multiplied by a respective normalization constant (60), which is inversely proportional to proportional to the Euclidean norm of the respective weighting vector (44), - for each neuron (22) the respective transfer function (42) is created based on the respective weighting vector (44), and - the transfer function (48) is processed with the activation function (48) such that the binary result (26) is provided.
  6. Method (52) according to claim 5, characterized by that at the beginning the weights (44) are chosen arbitrarily.
  7. Method (52) according to claim 5 or 6, characterized by that after performing the training steps, the weights (44) are binarized and multiplied by the sign of the respective normalization constant (60).
  8. Hearing aid (2) comprising a neural network (18) with several neurons (22) and operated according to a method (28, 52) according to one of claims 1 to 7.

Description

The invention relates to a method for operating a hearing aid with a neural network. Furthermore, the invention relates to a method for training a neural network and a hearing aid. People with hearing loss typically use a hearing aid. This usually involves an electromechanical transducer that captures ambient sound. The resulting electrical (audio) signals are amplified by an amplifier circuit and then delivered to the person's ear canal via another electromechanical transducer, often in the form of a receiver. The captured audio signals are usually also processed, typically by a signal processor within the amplifier circuit. The amplification is adjusted to the specific hearing loss of the hearing aid user, who is also referred to as the user or wearer. Depending on the current situation, it may be necessary to adjust the processing to improve intelligibility for the user. Especially with speech, it is desirable to choose different processing settings for different syllables or sounds. For example, reducing certain frequencies improves intelligibility for some syllables, while reducing intelligibility for others. One difficulty with this processing lies in distinguishing between speech components and background noise components in the audio processed by the hearing aid. Signal. Artificial neural networks (hereinafter simply referred to as neural networks) are particularly well-suited to solving this problem. Neural networks with feedforward components, where only the current input values influence the state, and recurrent components, where the state is also influenced by the results of past processing steps, are especially suitable for processing audio signals. However, for neural networks to be suitable for distinguishing speech and background noise components in the signal, very large networks with many layers are necessary; that is, the networks consist of a large number of neurons. This also applies to other typical applications of neural networks for audio signal processing. Due to their size, these neural networks have high hardware and energy requirements, which is undesirable for a hearing aid, as these should be as small and energy-efficient as possible. The invention is based on the objective of providing a particularly suitable method for operating a hearing aid, a particularly suitable method for training a neural network, and a particularly suitable hearing aid, wherein in particular user comfort is increased and hardware resources and/or energy requirements are advantageously reduced. With regard to the method for operating a hearing aid, this problem is solved according to the invention by the features of claim 1, with regard to the method for training a neural network by the features of claim 1, and with regard to the hearing aid by the features of claim 5 and with regard to the hearing aid by the features of claim 8. Advantageous further developments and embodiments are the subject of the respective dependent claims. The method is used to operate a hearing aid. For example, the hearing aid is a headphone or includes a headphone, and the hearing aid is, for example, a headset. However, the hearing aid is particularly preferred as a hearing assistance device. A hearing aid is used to support a person suffering from hearing loss. In other words, a hearing aid is a medical device that compensates for, for example, partial hearing loss. Hearing aids include, for example, receiver-in-the-canal (RIC) hearing aids, in-the-ear (ITC) hearing aids, complete-in-the-canal (CIC) hearing aids, hearing glasses, and pocket hearing aids. Alternatively, a hearing aid can be a behind-the-ear (BTE) hearing aid, which is worn behind the ear. The hearing aid is designed and configured to be worn on the human body. In other words, the hearing aid preferably includes a retention device that allows it to be attached to the human body. If the hearing aid is a hearing assistance device, it is designed and configured to be placed, for example, behind the ear or within an ear canal. In particular, the hearing aid is wireless and designed and configured to be inserted, at least partially, into an ear canal. The hearing aid preferably includes a microphone for capturing sound. In particular, when the hearing aid is operated using the microphone, ambient sound, i.e., sound waves, or at least a portion thereof, is captured. The microphone is advantageously located at least partially within a housing of the hearing aid and is thus at least partially protected. The microphone is suitably an electromechanical transducer. The microphone may, for example, have only a single microphone unit or several microphone units that interact with each other. Each of the microphone units advantageously has a diaphragm that is set into vibration by sound waves, the vibrations being converted into an electrical signal by means of a suitable recording device, such as a magnet moved within a coil. Alternatively, the microphone units are capa