51 results about "Audiogram" patented technology

The invention discloses a sound system with DSP sound effect enhancement processing, which belongs to the technical field of audio processing and comprises an audio input module, a data acquisition module, a DSP processing unit, an audio output module and a user interaction module. By acquiring characteristic data (such as audiogram and heart rate) of a hearing impaired person or a child in real time, a DSP processing unit dynamically calls a targeted algorithm (weak hearing frequency band compensation and voice rhythm adjustment), and a multi-frequency band equalization and noise suppression technology is combined, so that an optimized audio signal adaptive to user requirements is output. The problem that a traditional sound system cannot adapt to special crowds in a personalized mode is solved, the sound effect definition and user experience are remarkably improved, and the system is suitable for education, medical treatment and family scenes.

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 discloses a reinforcement learning hearing aid adaptation method in combination with a hearing loss speech evaluation model, and aims to design an optimal hearing aid adaptation gain matched with a listening environment based on the common listening environment of a hearing-impaired person and improve the hearing aid use effect of the hearing-impaired person. An audiogram of a subject is obtained through a hearing measurement tool, the gain configuration of the hearing aid is optimized by using a reinforcement learning algorithm, and finally a personalized gain adjustment scheme is provided for the subject. The method is based on a reinforcement learning algorithm iteration strategy network, the hearing aid gain is automatically adjusted, and it is ensured that optimal hearing experience is provided under different sound pressure levels and noise environments. By means of the hearing loss speech model, the gain adjustment effect can be evaluated in real time, and the speech perception effect is optimized. Compared with a traditional method, the method has the advantages that the common acoustic environment requirements of a patient can be accurately matched, the adaptation precision and effect are improved, and higher adaptability and wide clinical application prospects are achieved.

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.

A method for determining an acoustic mass of an earpiece to be plugged into an ear of a user comprises: receiving user data comprising at least an audiogram of the user; and inputting the user data into a machine learning algorithm and determining the acoustic mass by the machine learning algorithm.

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.

The invention relates to a multi-environment hearing aid gain prediction method and system, a processing device and a storage medium, and the method is characterized in that the method comprises the steps: obtaining an audiogram of a patient; inputting different hearing thresholds, different input sound pressure levels and environments in the audiogram of the patient into a trained multi-environment hearing aid gain prediction model to obtain prescription gain prediction results corresponding to the patient under different frequencies; the multi-environment hearing aid gain prediction model is constructed based on an XGBoost algorithm, a grid search method and a genetic algorithm, adaptive change of hearing aid gains in different environments can be realized, the prediction precision is high, the generalization performance is high, and the method can be widely applied to the field of gain compensation of hearing aid fitting.

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.

The present invention provides a hearing compensation device and a hearing compensation method, comprising: a transducer, which receives sound to convert the sound into an electrical signal; and a noise reduction module and a hearing compensation module, which are connected to the transducer to synchronously receive the electrical signal, wherein the noise reduction module generates an opposite signal with the same energy as the electrical signal to remove environmental noise, and the hearing compensation module automatically searches for multiple sets of parameters of multiple filters based on a real-time customized audiogram or audiometer obtained by the user in the current real environment through noise reduction technology combined with an optimization method and a loss function to generate optimal filter parameter values, so that the noise reduction module and the hearing compensation module set in the same or a single chip perform real-time and / or synchronous processing to effectively provide a hearing aid that reduces signal delay, is real-time and customized for the user.

The present invention relates to a hearing aid for the elderly to compensate for and improve hearing loss, wherein a user by himself / herself can select, as a selection means, one amplification gain value suitable for the user's own audiogram among a plurality of programs with divided amplification gain values. The hearing aid allows the user to hear by covering the user's ear with a main body of the hearing aid so as to improve speech comprehension and sound quality.

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.

Techniques and corresponding systems are disclosed for estimating an audiogram of a user of a media playback device, the techniques and corresponding systems comprising: obtaining user hearing threshold data, sample hearing threshold data, obtaining at least one of sample calibration data and sample noise data for the user; and determining an estimate of the audiogram of the user based on such data. Related techniques for estimating calibration data for a media playback device, as well as corresponding computing apparatuses, computer programs, and computer-readable storage media, are also described.

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.