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WO-2026093536-A1 - METHOD FOR ADAPTIVE ACTIVE NOISE SUPPRESSION

WO2026093536A1WO 2026093536 A1WO2026093536 A1WO 2026093536A1WO-2026093536-A1

Abstract

The invention relates to a method for adaptive active noise suppression for a portable audio system that has a microphone unit comprising at least one first microphone and a loudspeaker, a memory unit and a computing unit, the method comprising the following features: - providing evaluation data comprising audio data, the audio data being recorded using the microphone unit, the audio data comprising at least one first audio signal that is recorded using the first microphone of the microphone unit; - determining filter coefficients on the basis of the provided evaluation data using the computing unit; - generating filtered audio data from the recorded audio data using the filter coefficients and the computing unit; and - reproducing the filtered audio data using the loudspeaker.

Inventors

  • Fabry, Johannes
  • BRANDIS, Raphael Nicolas
  • Liebich, Stefan

Assignees

  • Elevear GmbH

Dates

Publication Date
20260507
Application Date
20251031
Priority Date
20241031

Claims (19)

  1. 1. Method (100) for adaptive active noise suppression for a portable audio system (10) comprising a microphone unit including at least a first microphone (18) and a loudspeaker (26), a storage unit and a computing unit (24), wherein the method (100) has the following features: Providing (110) evaluation data comprising audio data, wherein the audio data are recorded using the microphone unit, wherein the audio data include at least one first audio signal which is recorded with the first microphone (18) of the microphone unit; Determining (120) filter coefficients depending on the provided evaluation data using the computing unit (24); Generation (130) of filtered audio data from the recorded audio data using the filter coefficients and the computing unit (24); Playback (140) of the filtered audio data using the loudspeaker (26); wherein the determination of the filter coefficients is based on the use of a machine learning-based model; the machine learning-based model is stored in the memory unit; the machine learning-based model is trained with training data, wherein the training data includes training audio data and training filter coefficients associated with the training audio data, wherein the provided evaluation data are input into the machine learning-based model and, depending on the provided evaluation data, filter coefficients are output by the machine learning-based model.
  2. 2. Method (100) according to claim 1, characterized in that the method (100) comprises a training process for training the machine learning-based model.
  3. 3. Method (200) according to claim 2, wherein the training process comprises the following steps: Providing (210) training audio data comprising at least one initial audio signal; Determine (220) one set of filter coefficients for each of the provided training audio data; wherein the determination of each set of filter coefficients comprises a reduction of a cost function, wherein the cost function preferably describes a sound pressure level in the ear canal (14) of a user; Training (230) the machine learning-based model using the training audio data and the determined filter coefficient sets; and Storing (240) the trained model in a training memory unit.
  4. 4. Method (200) according to claim 3, characterized in that the reduction of the cost function comprises a reduction of a cost function that describes the sound pressure level in the external auditory canal (14) of a user.
  5. 5. Method (200) according to claim 2, wherein the training process comprises the following steps: Determination of filter coefficients by the model based on the audio data; Simulation of a sound pressure level in the ear canal based on the audio data and the determined filter coefficients; - 40 - Evaluation of a cost function based on the simulated sound pressure level, which correlates with the perceived loudness; Adjusting the model parameters to reduce the cost function.
  6. 6. Method (200) according to claim 5, wherein the training process comprises the following steps: Providing training audio data comprising at least an initial audio signal; Determine one set of filter coefficients for each of the provided training audio data; Simulation of a sound pressure level in the ear canal based on the audio data and the determined filter coefficients; Evaluation of a cost function based on the simulated sound pressure level, where the cost function represents the perceived loudness; and Adjustment of the model parameters taking into account the cost function.
  7. 7. Method (200) according to one of the preceding claims, characterized in that the first audio signal is provided as an audio signal converted into the frequency domain, wherein the conversion into the frequency domain is preferably carried out via a short-time Fourier transform.
  8. 8. Method (200) according to one of the preceding claims, characterized in that the audio data includes a second audio signal which is recorded with a second microphone (20) of the microphone unit.
  9. 9. Method (200) according to claim 8, characterized in that the audio data includes a third audio signal which is recorded with a third microphone (28) of the microphone unit.
  10. 10. Method (200) according to one of the preceding claims, characterized in that the evaluation data includes at least one transfer function that describes the transmission path between an external noise and the inner ear.
  11. 11. Method (200) according to one of the preceding claims, characterized in that the evaluation data includes adaptation data which characterize the adaptation of the audio system to the user's head and/or direction data which characterize the direction of incidence of a disturbance noise.
  12. 12. Method (200) according to one of the preceding claims, characterized in that the machine learning-based model comprises an artificial neural network, a support vector machine or a linear regression.
  13. 13. Method (200) according to one of the preceding claims, characterized in that the training audio data comprises several hundred, preferably several 1,000, particularly preferably several 10,000 training audio signals.
  14. 14. Audio system (10) for adaptive active noise reduction, comprising a microphone unit with at least one first microphone (18), a loudspeaker (26), a storage unit and a computing unit (24), wherein the microphone unit is designed to record evaluation data comprising audio data, wherein the audio data includes at least one first microphone (18). exhibit an audio signal, and the first microphone (18) of the microphone unit is designed to record the first audio signal; the processing unit (24) is designed to determine filter coefficients depending on the provided evaluation data; the processing unit is designed to generate filtered audio data from the recorded audio data using the filter coefficients; the loudspeaker (26) is designed to output the filtered audio data; wherein the processing unit (24) is designed to determine the filter coefficients using a machine learning-based model; the machine learning-based model is stored in the storage unit; the machine learning-based model is trained with training data, wherein the training data includes training audio data and training filter coefficients associated with the training audio data; the processing unit (24) is designed to input the provided evaluation data into the machine learning-based model and, depending on the provided evaluation data, to determine filter coefficients through the machine learning-based model.
  15. 15. Audio system (10) according to claim 14, characterized in that the microphone unit has a second microphone (20) designed to record a second audio signal of the evaluation data.
  16. 16. Audio system (10) according to claim 15, characterized in that the second microphone (20) is designed to detect sound within an ear canal (14) of a user.
  17. 17. Audio system (10) according to one of claims 14 to 16, characterized in that the microphone unit has a third microphone (28) designed to record a third audio signal of the evaluation data.
  18. 18. Audio system (10) according to claim 17, characterized in that the third microphone (28) is preferably designed to detect sound outside of an ear canal (14) of a user.
  19. 19. Audio system (10) according to any one of claims 14 to 18, characterized in that it is a headphone, headset, smartphone or hearing aid. Audio system (10) according to any one of claims 14 to 18, characterized in that it is a headphone, headset, smartphone or hearing aid.

Description

Methods for adaptive active noise suppression The present invention relates to a method for adaptive active noise suppression for an audio system and a corresponding audio system. Methods for noise suppression are widely known in various forms. A general distinction is made between passive and active noise suppression. While passive noise suppression uses sound-absorbing materials to shield external noise and thereby reduce the noise perceived by a user, active noise reduction uses a microphone to pick up external noise and analyzes it with a processing unit. This analysis then generates an out-of-phase signal that is output via a loudspeaker. This out-of-phase signal interferes with the noise in such a way that destructive interference occurs, and the noise is either eliminated or at least significantly reduced. Active noise reduction methods can be used, in particular, in headphones (also known as ANC or active noise-canceling headphones), headsets, hearing aids, and other audio systems. However, the use of the method according to the invention is explicitly not limited to the aforementioned devices. However, the method according to the invention can also be used in other audio systems where active noise reduction is desired. Active noise suppression methods can employ digital filters described by filter coefficients. The recorded noise is passed through the digital filter, which determines how the frequency components of the noise signal are modified in terms of magnitude and phase. Subsequently, an out-of-phase signal can be generated, which is then combined with the noise. destructively interferes and leads to a reduction in the background noise received by the user. From US 2023/0223001 Al, a signal processing device, a signal processing method, a signal processing program, a method for generating a signal processing model, and a sound reproduction device are known. Determining suitable filter coefficients for an individual application scenario (different background noise, different fit of the audio device (e.g., the fit of headphones), different directions of incidence of the background noise, etc.) presents a considerable challenge in practice. Based on the problem described above, the object of the present invention is to provide a method for adaptive active noise reduction for a portable audio device that allows for optimized provision of the filter coefficients. To solve the described problem, the present invention proposes a method for adaptive active noise suppression for a portable audio system, which comprises a microphone unit including at least one first microphone, a loudspeaker, a storage unit and a computing unit, wherein the method has the following features: Providing evaluation data comprising audio data, wherein the audio data is recorded using the microphone unit, wherein the audio data includes at least one first audio signal that is recorded with the first microphone of the microphone unit; Determining filter coefficients depending on the provided evaluation data using the computing unit; Generation of filtered audio data from the recorded audio data using the filter coefficients and the processing unit; Playback of the filtered audio data using the loudspeaker; wherein the determination of the filter coefficients is based on the use of a machine learning-based model; the machine learning-based model is stored in the memory unit; the machine learning-based model is trained with training data, wherein the training data includes training audio data and training filter coefficients associated with the training audio data; wherein the provided evaluation data is input into the machine learning-based model and, depending on the provided evaluation data, filter coefficients are output by the machine learning-based model. The method according to the invention offers the advantage that the optimal filter coefficients required for adaptive active noise suppression can be determined automatically and efficiently. The filter coefficients are determined depending on the current application scenario. For example, the spectral properties of the existing noise can be taken into account. Furthermore, as will be explained in detail below, the direction of incidence of the noise and the fit or positioning of the audio device can also be considered, particularly when multiple microphones are used, designed to record several audio signals at different positions. The audio system can be, in particular, headphones, a headset, or a hearing aid. However, the present invention is explicitly not limited to use in connection with the aforementioned audio systems, but can be used with any audio system where active noise reduction is desired. The audio systems can be worn on the ear (for example, in the case of headphones, a headset, or a hearing aid). They can be worn on headsets or smartphones) or in the ear (for example, in the case of a hearing aid). The microphone unit can include one or more micropho