17 results about "Vocal fold vibration" patented technology

The invention discloses a digital imaging method of an ear-nose-throat examination endoscope, and relates to the technical field of medical treatment, and the method comprises the following steps: synchronously collecting a white light image, a photoacoustic signal, a stimulated Raman spectrum, pressure sensor data and a physiological signal of a target orifice through a multi-physical field probe; constructing a four-dimensional tensor bound with the anatomical features; based on a vocal cord vibration fundamental frequency harmonic characteristic optimization graph convolutional network, dynamically constructing an adjacent matrix and coupling cross-modal characteristics to generate a submucosal lesion enhanced image; a deformable convolutional network constrained by vocal cord biomechanics is adopted, and the shape of a convolution kernel is dynamically adjusted according to the relation between real-time strain and elastic modulus, so that motion artifacts caused by swallowing actions are inhibited; generating a curvature-driven asymmetric convolution kernel based on ear canal spiral geometry, and executing super-resolution reconstruction in combination with confrontation training of fractal constraint; and fusing the white light image gradient and the pressure gradient field, and outputting a three-dimensional lesion contour consistent with the anatomical structure.

The invention relates to the technical field of earphone noise reduction, and discloses a bone conduction earphone intelligent noise reduction method and system based on multi-mode interactive fusion, and the method comprises the steps: collecting a multi-mode signal when a user uses a bone conduction earphone, carrying out the motion artifact elimination of the multi-mode signal, and obtaining a pure multi-mode signal; identifying a vocal cord vibration fundamental frequency and a formant of the pure multi-mode signal to perform noise fusion characterization processing on the bone conduction earphone to obtain a noise characterization matrix, and identifying a noise type of the bone conduction earphone by using the noise characterization matrix; analyzing a physiological fatigue index of the user, constructing noise reduction parameters of the bone conduction earphone based on the physiological fatigue index and the noise type, and constructing a noise reduction propagation link of the bone conduction earphone; and carrying out anti-howling adaptive filtering on the noise reduction propagation link by using the noise representation matrix to obtain a reverse sound wave, and carrying out noise reduction on the bone conduction earphone to obtain a noise reduction audio. According to the invention, the bone conduction earphone wearing experience of the user can be improved.

The invention discloses a voice interaction method and system based on an intelligent doll, and relates to the technical field of intelligent acoustic interaction, and the method comprises the steps: carrying out the time-frequency analysis processing of a vibration feature matrix, and generating a modal parameter set of vocal cord vibration through biomechanical modeling in combination with the human neck tissue density features; inputting the modal parameter set of vocal cord vibration into the vibration-acoustic model to generate a sound source excitation field, performing environmental noise compensation on the sound source excitation field by using the acoustic characteristic matrix, and generating a time domain pure voice signal through an acoustic wave equation; and collecting real-time position coordinates of the user, calculating and driving a piezoelectric loudspeaker array of the intelligent doll to adjust the phase according to the frequency spectrum characteristics of the time domain pure voice signal, forming a directional focusing sound field, recording user physiological feedback, and generating a user physiological feedback matrix. According to the method, the vocal cord displacement modal function vector is solved through the nonlinear integral and augmented Lagrange algorithm, so that the acquisition precision and the anti-noise capability of the voice signal are improved from the source.

The application discloses a sound source positioning and identification method and device based on multi-modal fusion and a storage medium, relates to the technical field of sound source positioning, and discloses a sound source positioning and identification method based on multi-modal fusion, which comprises the following steps: detecting a biological target with lip movement characteristics and / or vocal cord vibration characteristics in a scene through a millimeter wave radar, and acquiring the spatial coordinates of the biological target; acquiring the sound source direction angle of a sound source through a microphone array, and generating a corresponding sound source direction vector based on the sound source direction angle; judging whether the biological target is an effective sound production target based on the spatial relationship between the spatial coordinates and the sound source direction vector; and if the biological target is the effective sound production target, determining the identity information of the effective sound production target. The application effectively fuses multi-modal data such as radars and audio, significantly improves the accuracy of sound source positioning and identification, and realizes the technical effects of stable and reliable speaker positioning and identity recognition in a complex environment.

The invention discloses a nasal sound recognition method based on acoustic feature decoupling, which is characterized in that a double-flow decoupling deep neural network is constructed, a mutual information minimization adversarial training strategy is introduced, and sound source features (vocal cord vibration) and sound channel features (oral cavity and nasal cavity adjustment) are forcibly separated from a single audio signal. On the basis, the high-resolution characteristic of CQT is used for accurately extracting acoustic characteristics related to the nasal sound, physiological offset correction is carried out in combination with personal acoustic fingerprints, and finally accurate quantification and recognition of different nasal sound states (normal, defect and skill) are achieved, so that an objective and visual feedback basis with artistic style adaptability is provided for vocal music teaching.

The invention belongs to the technical field of pronunciation disorder vocal cord vibration models, and particularly relates to a pronunciation disorder vocal cord vibration model establishing system which comprises an image acquisition module used for acquiring vocal cord vibration images by using a high-definition electronic stroboscopic laryngoscope; the image preprocessing module is used for performing enhancement, noise reduction and segmentation processing on the image acquired by the image acquisition module; and the vocal cord vibration feature extraction module is used for extracting vocal cord features according to the image processed by the image preprocessing module. The vocal cord region can be accurately identified and segmented by using a semantic segmentation algorithm based on deep learning, interference of complex backgrounds such as surrounding tissues and secretions can be effectively eliminated, the vocal cord tissues and the backgrounds can be accurately distinguished even if mucus adheres to the periphery of the vocal cord, accurate image data can be provided for subsequent feature extraction, and the vocal cord recognition accuracy is improved. And the reliability and accuracy of feature extraction are improved.

The invention relates to the technical field of medical care and rehabilitation medicine, and discloses a method for establishing reverse airflow to assist sound production through a subglottic suction tube, and the method comprises the following steps: executing airway cleaning, keeping a tracheotomy cannula air bag full, and blocking a tracheal gap by using the air bag; constructing a reverse air supply passage for connecting an external air source and the subglottic suction tube; gas supply equipment is started, and the gas flow is adjusted through a progressive strategy; on-off of a gas path is controlled according to sound production requirements, so that gas is ejected from the subglottic suction tube and is limited by blocking of the air bag to form upward reverse airflow to impact the vocal cord. The air bag is used as a physical barrier to reconstruct subglottic positive pressure, vocal cord vibration is effectively driven to recover the speech function of a patient on the premise that a tracheotomy ventilation mode is not changed and the risk of aspiration by mistake is not increased, the problem that the tracheotomy patient cannot make a sound for a long time is solved, and meanwhile recovery of physiological reflex of the upper respiratory tract is promoted through fluid stimulation.

The application discloses a voice interaction method and system based on an intelligent doll, relates to the technical field of intelligent acoustic interaction, and comprises the following steps: performing time-frequency analysis and processing on a vibration feature matrix, combining with the density characteristics of human neck tissues, and generating a modal parameter set of vocal cord vibration through biomechanical modeling; inputting the modal parameter set of vocal cord vibration into a vibration-acoustic model to generate a sound source excitation field, compensating the sound source excitation field for environmental noise by using an acoustic feature matrix, and generating a time-domain pure speech signal through a sound wave equation; collecting real-time position coordinates of a user, calculating and driving a piezoelectric loudspeaker array of the intelligent doll to adjust a phase according to the spectral characteristics of the time-domain pure speech signal, forming a directional focused sound field, recording physiological feedback of the user, and generating a user physiological feedback matrix. The application solves a vocal cord displacement modal function vector by using a nonlinear integral and an augmented Lagrange algorithm, so that the collection accuracy and the noise resistance of a speech signal are improved at the source.

The application relates to the technical field of speech recognition, in particular to a speech-based emotion recognition method, which comprises the following steps: according to an input speech signal, frame division and windowing are carried out. The application can obtain more profound insights into emotional expression by decomposing the input speech signal into two physical sources of glottal excitation and vocal tract response for independent modeling and analysis, obtaining a vocal tract transfer function set via linear predictive coding operation, and applying inverse filtering to the original speech signal to reconstruct an approximate glottal pulse sequence, effectively stripping the influence of vocal tract resonance on the signal, so that perturbation parameters, open quotient and closed quotient and the like representing vocal cord vibration patterns can be directly calculated, at the same time, the vocal tract transfer function set is used to identify and track the dynamic trajectory of the formant, and the phase difference cosine mean between adjacent frames is combined to quantify the sound production stability, and subtle dynamic adjustment and control stability of sound production organs such as the oral cavity and tongue position caused by emotional changes are captured.

The invention discloses an ear-nose-throat disease early screening method. The method comprises the following steps: a voice acquisition step: acquiring a standardized voice sample of a user through an audio acquisition device; a feature extraction step: extracting multi-level acoustic features including vocal cord vibration features, resonance features, pronunciation stability features and depth representation features from the voice sample; and a baseline establishment step. According to the ear-nose-throat disease early-stage screening method and the ear-nose-throat disease early-stage screening system provided by the embodiment of the invention, the influence of individual voice difference on the screening result is effectively overcome by establishing the individual dynamic voiceprint base line; multi-level acoustic feature analysis is adopted, so that the accuracy of disease recognition is improved; continuous monitoring and dynamic risk assessment are realized, and possibility is provided for early lesion discovery; on the premise of protecting user privacy, model optimization is realized through federal learning; an objective and quantitative auxiliary diagnosis basis is provided for clinicians, and the diagnosis and treatment efficiency is improved.

The invention discloses a sound source localization identification method and device based on multi-modal fusion, and a storage medium, relates to the technical field of sound source localization, and discloses a sound source localization identification method based on multi-modal fusion, and the method comprises the steps: detecting a biological target with a lip movement feature and / or a vocal cord vibration feature in a scene through a millimeter wave radar; obtaining the space coordinates of the biological target; obtaining a sound source direction angle of a sound source through a microphone array, and generating a corresponding sound source direction vector based on the sound source direction angle; judging whether the biological target is an effective sounding target or not based on the spatial relationship between the spatial coordinates and the sound source direction vector; and if the biological target is the effective sounding target, determining identity information of the effective sounding target. According to the method, radar, audio and other multi-mode data are effectively fused, the accuracy of sound source positioning and recognition is remarkably improved, and the technical effect of stable and reliable speaker positioning and identity recognition in a complex environment is achieved.

The present invention relates to the technical field of vocal cord vibration models for articulation disorders, specifically a system for establishing a vocal cord vibration model for articulation disorders, comprising: an image acquisition module for acquiring vocal cord vibration images using a high-definition electronic stroboscopic laryngoscope; an image preprocessing module for enhancing, denoising, and segmenting the images acquired by the image acquisition module; and a vocal cord vibration feature extraction module for extracting vocal cord features based on the images processed by the image preprocessing module. The present invention utilizes a semantic segmentation algorithm based on deep learning to accurately identify and segment the vocal cord region, effectively eliminating interference from complex backgrounds such as surrounding tissues and secretions. Even in the presence of mucus adhesion around the vocal cords, the system can accurately distinguish between vocal cord tissue and background, providing accurate image data for subsequent feature extraction and improving the reliability and accuracy of feature extraction.

The present invention relates to a high-quality voice signal processing device and method through ambient noise removal based on multi-sensor signal fusion, which enables voice signal processing robust to external noise environments through multi-sensor signal fusion using an accelerometer sensor (ACC) and a voice microphone sensor (MIC). The device comprises: a voice microphone sensor (MIC) that senses and outputs a voice signal of a speaker; an accelerometer sensor (ACC) that detects vocal cord vibration of a speaker and outputs a signal; a noise reduction processing MCU that extracts a vocalization segment based on vocal cord vibration using the output signal of the accelerometer sensor (ACC), synthesizes the low-frequency component of the accelerometer sensor (ACC) and the low-frequency component of the voice microphone sensor (MIC) by varying the synthesis ratio based on the noise level extracted from the output signal of the voice microphone sensor (MIC) using the vocalization segment information, and restores and outputs a voice signal by adding the synthesized low-frequency component and the high-frequency component of the voice microphone sensor (MIC); and a wireless communication module that outputs the restored voice signal externally.

The present invention discloses a multi-modal speech recognition system and method based on a millimeter-wave radar. The system includes a feature extraction module and a multi-modal fusion and recognition module. The feature extraction module uses the millimeter-wave radar to transmit a frequency-modulated continuous wave signal and extracts lip movement features and vocal cord vibration features from the reflected signal. The multi-modal fusion and recognition module is used to fuse the lip movement features and vocal cord vibration features and perform speech recognition. By fusing the lip movement feature and vocal cord vibration feature technologies, the present invention achieves the complementary and enhanced effects of the two features, further improving the accuracy of speech recognition.

The semantic recognition method and related apparatus provided in this application relate to the field of terminal technology. The method includes: acquiring data such as the vibration frequency of a user's vocal cords based on the radar of an electronic device, and parsing the radar-acquired data to obtain the semantics the user wants to express. Using radar to detect vocal cord vibration offers good privacy and transmission capabilities. This allows the electronic device to not rely entirely on processing voice signals or lip movements, enabling more accurate semantic recognition even in noisy, dimly lit, or obstructed environments, improving call quality and thus enhancing the user experience.

The application provides a vehicle-mounted intelligent sound field regulation method based on brain waves and surface acoustic waves, comprising: acquiring an acoustic signal in the vehicle and a brain electrical signal of a driver, wherein the acoustic signal is collected by a surface acoustic wave sensor, and the brain electrical signal is acquired by a brain electrical collection device; processing the acoustic signal, extracting mechanical vibration features related to vocal cord vibration, and distinguishing between a vocal signal and a non-vocal signal based on the mechanical vibration features; processing the brain electrical signal, extracting brain electrical features representing the attention state and / or physiological state of the driver; performing multi-modal fusion analysis on the acoustic feature information corresponding to the vocal signal and the brain electrical features, generating a sound field regulation strategy and / or a safety response strategy corresponding to the state of the driver; adjusting the vehicle-mounted audio output according to the sound field regulation strategy to enhance the vocal signal and / or suppress the non-vocal signal; and / or triggering a corresponding safety response operation according to the safety response strategy.