36 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 present invention discloses a speech enhancement method that combines hearing loss compensation and speech noise reduction, comprising: extending and embedding a hearing loss map along the frequency axis to obtain a hearing loss spectrum, and superimposing the hearing loss spectrum with the complex spectrum features of the noisy training speech; constructing a metric generative adversarial network model based on frequency-time convolution recursion, the model's main structure comprising a compensation generator and a metric discriminator; alternately training the compensation generator and the metric discriminator to optimize the metric generative adversarial network model; superimposing the complex spectrum features of the test speech with the hearing loss spectrum and inputting them into the trained compensation generator, and reconstructing an enhanced speech waveform of the test speech based on the output of the compensation generator. The present invention uses a metric generative adversarial network to simultaneously achieve noise reduction and hearing loss compensation for a specific audiogram, capable of stably and effectively improving the effectiveness of hearing loss compensation in noisy environments. The method is ingenious and novel, with promising application prospects.

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.

A novel system and method for identifying and establishing personalized audio parameter settings for a person with hearing loss and / or certain listening preferences, without requiring a traditional audiogram or hearing test as a starting point. Instead, two new elements are combined: (1) A group of original sample profiles, and (2) An original tuning flow methodology and algorithm. The tuning flow methodology and algorithm includes a decision-tree conditional logic and structured series of questions that leverage a) the group of sample profiles, and b) new approaches to user interface interaction.

A hearing aid system is disclosed that automates selection or creation of hearing profiles using a dynamically customizable vivo adaptare audiogram. The hearing aid and a smart device communicate via a programming interface, where the smart device runs processor-executable instructions for a listening intelligence and frequency enhancement function. An artificial intelligence module evaluates ambient sound, compares it to stored hearing profiles specifying frequency adjustments, and either selects one or merges multiple profiles to form a new profile. The chosen profile is activated in real time, with the option to store it for later use. Available modes include “Test and Store,” capturing ambient sound to refine a profile, and real-time adaptation, which continuously applies incremental changes without repeated user input.

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.

An exemplary system comprises memory that stores instructions and a processor communicatively coupled to the memory and configured to execute the instructions to perform a process. The process may comprise obtaining a pre-operative audiogram of a recipient, the pre-operative audiogram generated prior to a lead insertion procedure during which an electrode lead having a plurality of electrodes is inserted into a cochlea of the recipient, obtaining one or more attributes associated with the lead insertion procedure, and determining, based on the pre-operative audiogram and the one or more attributes, one or more stimulation frequencies for stimulation that is to be used during the lead insertion procedure to elicit evoked responses within the recipient, the evoked responses usable to monitor an insertion condition associated with the electrode lead.

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).

The present disclosure provides systems and methods for adjusting the audio output of a wearable device based on a user's audio gain profile. The wearable device may receive an audiogram indicating one or more frequency ranges associated with hearing loss. The wearable device may determine the audio gain profile based on the audiogram. The wearable device may use one or more adjustment modules to determine a gain to apply to the audio output based on the audio gain profile. The adjustment modules may include an active noise control module, a hearing aid module, and a transparency control module. The wearable device may use a least mean square algorithm and / or a machine learning model to determine the amount of gain to apply.

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.

The invention discloses an automatic identification and diagnosis method and system for sonometer software data and a storage medium, and the method comprises the steps: identifying a running sonometer software window based on window features, calling a related interface of an operation system, and capturing original image data containing an audiogram in an interface of the sonometer software window; selecting a pre-input parameter combination according to the acquired type and version of the sonometer software; extracting a left ear audiogram and a right ear audiogram in the original image by combining computer graphics based on parameter combination; the left ear audiogram and the right ear audiogram are respectively processed by utilizing computer vision and combining with a deep learning method, coordinates and types of all measurement points in the audiograms are identified, hearing threshold data under all frequencies are obtained through reduction, hearing typing and indexing are carried out, and finally a diagnosis report is generated. According to the method, automation of the whole process from sonometer software data acquisition to diagnosis is achieved.

An audiogram (20), a system (10) for aiding hearing and a method for use of the same are disclosed. In one embodiment, a hearing aid device (12) equipped with sound processing capabilities, including a microphone (138), speaker (140), and electronic signal processor (130) wirelessly communicates with a smart device (14). The electronic signal processor (130) is adaptable based on a custom audiogram - vivo adaptare audiogram (20) - stored at the hearing aid device (12), which can be dynamically adjusted through a smart device application. Patients can directly influence their hearing experience by modifying the vivo adaptare audiogram (20) via the app, adjusting settings like sound level, directional microphone activation, and noise cancellation levels. This allows users to tailor hearing aid settings to the immediate environment and personal hearing needs, ensuring an optimized auditory experience. The system (10) emphasizes user control and adaptability, offering a significant advancement in hearing aid technology.

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.

A hearing aid system is disclosed that automates selection or creation of hearing profiles using a dynamically customizable vivo adaptare audiogram. The hearing aid and a smart device communicate via a programming interface, where the smart device runs processor-executable instructions for a listening intelligence and frequency enhancement function. An artificial intelligence module evaluates ambient sound, compares it to stored hearing profiles specifying frequency adjustments, and either selects one or merges multiple profiles to form a new profile. The chosen profile is activated in real time, with the option to store it for later use. Available modes include “Test and Store,” capturing ambient sound to refine a profile, and real-time adaptation, which continuously applies incremental changes without repeated user input.

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.

A cloud-based hearing aid management system and a method thereof is provided. The system includes a user device configured to output audiometry information and generate an audiogram based on a user's input indicating that if the audiometry information can be heard. The user device transmits the audiogram to a management server, such that the audiogram is converted into a hearing parameter, and hearing-loss characteristic value are extracted from the audiogram. The management server incorporates the hearing-loss characteristic value into a hearing aid recommendation table to generate corresponding hearing aid recommendation information. The present invention can recommend a hearing aid suitable for the user by operating a remote computer. In addition, the fitting staff adjusts the sound producing parameters of the hearing aid according to the user's requirement, which provides a convenient new consumption method.

The invention relates to a DPOAE otoacoustic emission testing method, electronic equipment and a medium, and the method comprises the steps: providing sounders, and outputting a dual-frequency stimulation sound signal to each sounder through a USB sound card; double-frequency stimulation sound is sent to the auditory meatus of the subject, the frequency ratio f2: f1 is 1.20-1.25, and each sounder sends out stimulation sound with the specific frequency; receiving an otoacoustic emission signal reflected back from the auditory meatus by using a sound receiver; inputting the signal into a preprocessing circuit for preprocessing; converting the preprocessed signal into a digital signal through an A / D converter, and transmitting the digital signal to a PC; collecting signals for multiple times in the PC and calculating an average value; performing Fourier transform on the averaged time domain signal to obtain a frequency spectrum; extracting frequency components of the stimulation sound and the otoacoustic emission signal from the frequency spectrum, and determining the intensity of the otoacoustic emission signal; the otoacoustic emission signal strength is compared to the noise level to generate a DPOAE audiogram. According to the method, the test accuracy and efficiency are improved, and the operation is simplified.

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.