24 results about "Audiogram" patented technology

An earpiece is configured for providing audiometric testing. The earpiece includes an earpiece housing, an intelligent control system disposed within the earpiece housing, at least one transducer operatively connected to the intelligent control, and at least one speaker operatively connected to the intelligent control. The earpiece is configured to perform audiometric testing of a user by reproducing sounds at the at least one transducer and receiving user feedback regarding the sounds to provide audiometric test data.

The invention relates to a method for creating an audiogram of a test person by means of a hearing instrument (1), in which a sound signal (22) is received by a first electro-acoustic input transducer (10) of the hearing instrument (1) in an ear canal (4) of the test person, which ear canal is at least partially closed by the hearing instrument (1), and a first input signal (24) is generated therefrom, a test sound (32) is generated by an electro-acoustic output transducer (18) of the hearing instrument (1) and is output into an ear canal (4) of the test person, which ear canal is at least partially closed by the hearing instrument (1), and wherein the hearing threshold of the test person at the at least one test frequency is determined on the basis of a reaction of the test person to the test sound (32) and by means of the first input signal (24).

The invention discloses a hearing aid speech enhancement method and device based on artificial intelligence, and a medium, and relates to the technical field of speech signal processing, and the method comprises the steps: inputting a standardized speech enhancement input feature into a direction perception high-frequency speech enhancement model, extracting a coding feature through a shared coding part in the direction perception high-frequency speech enhancement model, and carrying out the recognition of the coding feature; determining a target sound source direction through a direction information generation part in a direction perception high-frequency speech enhancement model, converting the target sound source direction into direction-related information, and combining the direction-related information with the encoding features to generate direction enhancement features; and inputting the direction enhancement feature into a high-frequency recovery part of a direction perception high-frequency speech enhancement model, performing enhancement processing on a high-frequency band corresponding to the target sound source to obtain high-frequency enhancement data, and compensating the high-frequency enhancement data according to the audiogram of the wearer to obtain individualized high-frequency enhancement frequency domain amplitude data. According to the invention, the recognition capability of the wearer on the target voice is improved.

Presented herein are systems and methods of providing acoustic stimulation to a human subject in order to evoke an auditory response. The method includes generating a chirp signal, where generating the chirp signal includes adding a plurality of frequency signals, each frequency signal being delayed in the chirp signal based on its associated frequency-specific basilar membrane delay, the frequency-specific basilar delay being determined as a function of an in-vivo frequency-specific basilar membrane delay.

1. Name of the product in this design: Graphical User Interface for Audiogram Drawing in Electronic Devices. 2. Application of this design: This design is intended for use in electronic devices. 3. The key design features of this product are its graphical user interface in electronic devices. 4. Images or photos that best illustrate the design points: Design 1 Interface change state diagram 2. 5. Electronic devices are designed in a conventional way, so other views are omitted. 6. Design 1 is designated as the basic design. 7. Purpose of the graphical user interface: This product design is mainly used for drawing audiograms to assess hearing loss. Interface interaction process: Drag your finger or mouse pointer within the audiogram area of ​​the main view of Design 1 to draw points and lines. At this time, the interface jumps to the interface change state diagram 1 of Design 1, which shows that the audiogram is being drawn. After the audiogram is completed in the interface change state diagram 1 of Design 1, it jumps to the interface change state diagram 2 of Design 1, which shows the complete audiogram and the corresponding binaural assessment results. The interaction process and purpose of Design 2 are the same as those of Design 1. 8. Other situations requiring explanation: Design 2 in this design is a partial appearance, and the area covered by the red coating is not within the protection scope.

Methods, systems, computer-readable media, and apparatuses for audio signal processing are presented. An apparatus for audio signal processing includes a memory configured to store instructions and a processor configured to execute the instructions. The instructions, when executed, cause the processor to receive an external microphone signal from a first microphone and generate a transmissive component based on the external microphone signal and hearing compensation data. The hearing compensation data is based on a hearing map of a particular user. The instructions, when executed, also cause the processor to cause a speaker to generate an audio output signal based on the transmissive component.

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 estimation system (100) adapted to use a latent representation to provide a predictive distribution of a complete audiogram for a specific person.

A system and method for diagnosing hearing impairments in individuals, particularly school-aged children, using voice sample analysis and data from electronic questionnaires. The system utilizes a deep neural network to process extracted acoustic features from voice recordings and correlate them with hearing performance data, generating a predicted audiogram. The system includes a microphone, signal acquisition and processing blocks, a neural network-based analysis engine, and a result interpretation interface.

The present invention aims to provide a method, apparatus, system, etc., for detecting indicators of acoustic neuroma by standardizing audiogram images or their constituent data and inputting them into an AI model. [Solution] An index detection method comprising: a storage step in which a computer stores images of audiogram-related information or their constituent data, which are information relating to an audiogram obtained about a target person, in a database; a standardization step in which data obtained from the audiogram images or the constituent data are standardized; an index detection step in which the standardized audiogram-related information is used as input to a trained model obtained by performing a learning process using data which is a combination of previously acquired and standardized audiogram-related information and an index of acoustic neuroma for that person, thereby detecting an index of acoustic neuroma for the target person; and a detection step in which the data is input to an AI model to detect an index of acoustic neuroma.

An earpiece is configured for providing audiometric testing. The earpiece includes an earpiece housing, an intelligent control system disposed within the earpiece housing, at least one transducer operatively connected to the intelligent control, and at least one speaker operatively connected to the intelligent control. The earpiece is configured to perform audiometric testing of a user by reproducing sounds at the at least one transducer and receiving user feedback regarding the sounds to provide audiometric test data.

The invention relates to a method for creating an audiogram of a test person by means of a hearing instrument (1), wherein a sound signal (22) is received in the ear canal (4) of the test person, which is at least partially closed by the hearing instrument (1), by means of a first electroacoustic input transducer (10) of the hearing instrument (1) and a first input signal (24) is generated therefrom, wherein a test sound (32) is generated by means of an electroacoustic output transducer (18) of the hearing instrument (1) and is output into the ear canal (4) of the test person, which is at least partially closed by the hearing instrument (1), and wherein the hearing threshold of the test person at at least one test frequency is determined from the reaction of the test person to the test sound (32) and by means of the first input signal (24).

Video game audio output is modified by obtaining a hearing loss frequency profile (eg. an audiogram) associated with a user, determining a loudness level for each frequency band of the audio output an

The application discloses a kind of professional noise exposure individualized noise reduction earplug self-adapting filtering method and system, its method includes: outward microphone picks up environmental sound stream;DSP carries out real-time acoustic scene decomposition, decomposes sound using time-frequency analysis, and identifies "human voice", "alarm" and other safety key acoustic events in real time by lightweight CNN;DSP combines personal audiogram, and executes frequency band adaptive noise reduction strategy: attenuates reverse wave to background noise frequency band;Safety key acoustic event frequency band is removed from noise reduction, and is played after individualized gain compensation according to personal audiogram.The application realizes the perfect fusion of hearing protection and context awareness, while protecting hearing, distortionlessly retains key safety information, greatly improves the wearing compliance and work safety of noise reduction earplug.

Disclosed are a multi-dimensional audiogram providing method and a apparatus. The apparatus includes a processor; and a memory connected to the processor, and the memory stores program instructions that, when executed, cause the processor to output a pure tone audiogram related to a hearing threshold for a subject measured in N frequency bands (wherein, N is a natural number of 15 or more) on a first surface of a polyhedron; to define a plurality of harmonic templates by rearranging the N frequency bands into a frequency set including a plurality of element frequencies having an integer ratio relationship; to calculate a standard deviation of hearing thresholds of a plurality of element frequencies in each of the plurality of harmonic templates to calculate a harmonic template instability degree; to output the calculated harmonic template instability degree on a second surface of the polyhedron; to calculate an average of the hearing thresholds of the plurality of element frequencies in each of the plurality of harmonic templates to calculate a harmonic template hearing; and to output the calculated harmonic template hearing on a third surface of the polyhedron.

The invention discloses a dual-tone source self-adaptive sound mixing optimization method for a hearing aid microphone and a charging box microphone. The method comprises the following steps: a charging box wirelessly transmits microphone audio collected by the microphone of the charging box to a hearing aid; and the hearing aid performs sound mixing optimization processing on the audio of the microphone of the charging box and the audio of the human ear acquired by the microphone of the hearing aid according to the audiogram information of the patient, and then outputs the audio. According to the method, the audiogram of the patient is combined during dual-sound-source sound mixing, and the influence of hearing loss of the patient on a sound mixing strategy is fully considered; a deep neural network structure for performing double-sound-source sound mixing in combination with an audiogram of a patient is adopted for performing double-sound-source sound mixing, so that the network fully captures voice and noise information, and noise suppression and speech definition are greatly improved while the robustness of a sound mixing system is improved.

1. The name of the design product: graphical user interface for measuring the hearing ability of human ear for electronic equipment. 2. The use of the design product: for electronic equipment. 3. The design points of the design product: the graphical user interface in the electronic equipment. 4. The picture or photo that best indicates the design points: interface change state diagram 3. 5. The use of the graphical user interface: measure the hearing ability of human ear, draw audiogram according to the hearing condition of the user, and conduct hearing loss evaluation. 6. The human-computer interaction mode of the graphical user interface: click the "OK" button of the main view to jump to interface change state diagram 1; click the "Save" icon of interface change state diagram 1 to jump to interface change state diagram 2, and click the "Save" icon of interface change state diagram 2 to jump to interface change state diagram 3.

An adaptive voice extraction and TTS adaptation system based on audiogram includes a sound collection and content extraction module, an audiogram adaptive TTS synthesis module, a wireless transmission and secondary gain module, and an audiogram test tool; the sound collection and content extraction module is used for collecting sound signals in different scenes, and identifying the corresponding speaker identity for each piece of text information; the audiogram test tool tests the hearing of the listener, outputs the audiogram parameters of the listener, the audiogram adaptive TTS synthesis module receives the text information, the corresponding speaker identity and the audiogram parameters, and generates a personalized TTS sound file; the wireless transmission and secondary gain module is in communication connection with the audiogram adaptive TTS synthesis module, and after receiving the TTS sound file, the secondary spectral gain adjustment is carried out according to the audiogram parameters. The present application can effectively shield environmental noise and realize individualized adaptation of audiogram.

The invention discloses a non-intrusive binaural speech evaluation method based on multi-source sound image fusion, which belongs to the technical field of audio signal processing and deep learning, and comprises the following steps of: extracting high-dimensional weighted representation of speech to be detected by using a leading-edge pre-training self-supervised learning speech basic model, extracting a frequency spectrum amplitude, and extracting a high-dimensional weighted representation of the speech to be detected; and the personalized audiogram data and the personalized audiogram data expanded along the frequency dimension are jointly used as the input of a feature fusion module in the binaural speech evaluation network so as to deeply mine the frame-level potential features of the speech to be detected. A dual-branch evaluation network is constructed: one combines the fused features with SSL representation to predict a speech quality score, and the other combines the fused features with audiogram data to output a quality level. According to the method, synchronous evaluation of the hearing aid voice quality score and grade is effectively realized, the Pearson linear correlation and the Spearman monotonous correlation between the predicted value and the real evaluation index are obviously improved, and a high-precision solution is provided for personalized hearing evaluation.

The disclosure provides a signal processing method for hearing aid, comprising: receiving an incoming sound signal; providing a listener audiogram; amplifying the incoming sound signal based on a representation of the listener audiogram and features of the incoming sound signal by using a neural network; and outputting an amplified sound signal from the neural network.

Video game audio output is modified by obtaining a hearing loss frequency profile (eg. an audiogram) associated with a user, determining a loudness level for each frequency band of the audio output an

The utility model relates to the technical field of hearing testing equipment, in particular to testing equipment capable of supporting automatic hearing testing, which comprises a host and earphones. A man-machine interaction module; the data acquisition module is used for generating an audiogram and hearing loss degree data according to the sound signal generated by the audio generation module and the response data of the subject; the storage module is used for storing and uploading the current test data and synchronizing with the cloud to read the historical data of the subject; the processor is used for comparing the historical data read by the storage module with the subject data input by the man-machine interaction module and the audiogram and hearing loss degree data generated by the data acquisition module, and adjusting the sound signal generated by the audio generation module; the data acquisition module supplements the missing part of the current audiogram and the hearing loss degree data or determines different parts again, and then the corresponding type is compared according to the existing case record and a report is generated.

The application discloses an artificial intelligence-based hearing aid speech enhancement method and device and a medium, relates to the technical field of speech signal processing, and comprises the following steps: inputting standardized speech enhancement input features into a direction perception high-frequency speech enhancement model; extracting coding features through a shared coding unit in the direction perception high-frequency speech enhancement model; determining a target sound source direction through a direction information generation unit in the direction perception high-frequency speech enhancement model, converting the target sound source direction into direction-related information, combining the direction-related information with the coding features, and generating direction enhancement features; inputting the direction enhancement features into a high-frequency recovery unit of the direction perception high-frequency speech enhancement model, performing enhancement processing on a high-frequency band corresponding to the target sound source, obtaining high-frequency enhancement data, compensating the high-frequency enhancement data according to a wearer's audiogram, and obtaining individualized high-frequency enhancement frequency domain amplitude data. The application improves the wearer's recognition ability for target speech.

This invention provides an automated pure-tone audiometry method based on Bayesian active learning and Gaussian process regression, comprising: performing a pre-test and establishing a regression model using a Gaussian process, and constructing an audiogram using the regression data; using the principle of Bayesian active learning to screen the iteration point with the greatest uncertainty, and obtaining the audiometric signal frequency and hearing level; performing audiometry on the subject to obtain data and adding it to the dataset; performing Gaussian process regression and active learning iterations until the target audiogram is obtained. This method replaces the fixed steps of traditional audiometry methods with the predictive power of machine learning, which can shorten the test time, reduce the test inaccuracy that may be caused by long test times, and provide a smooth and accurate audiogram with a much higher accuracy than traditional audiometry methods.