12424 results about "Microphone" patented technology

An online social wager-based gaming system is disclosed, featuring dynamic cross-provider game filtering and persistent cross-provider voice-interactive group play. The system allows for automated multi-seat group game reservations and incorporates a distributed ledger for bet verification. Features include a Group Connect module for dynamic group formation and live audio across various game providers, enabling real-time play. A Professional Companion Connect module facilitates live webcam or microphone gambling sessions with professional companions, under contractual agreements. The User Games module enhances personalization by integrating user-uploaded images into the gaming environment. A cryptocurrency / blockchain-based bet tracking system ensures transparency and security in managing bet transactions through a controlled digital wallet system. This integrated platform aims to provide a cohesive, interactive, and personalized online casino experience, addressing limitations of traditional solitary online gaming by fostering social connections and enhancing user trust and engagement.

Systems, methods, and non-transitory computer readable media including instructions for interpreting facial skin micromovements are disclosed. An example head mountable system includes a housing configured to be worn on a head of a wearer. The head mountable system also includes at least one detector integrated with the housing and configured to receive light reflections from a facial region of the head and to output associated reflection signals. The head mountable system also includes at least one microphone associated with the housing and configured to capture sounds produced by the wearer and to output associated audio signals. The head mountable system also includes and at least one processor in the housing, configured to use both the reflection signals and the audio signals to generate output that corresponds with words articulated by the wearer.

The present invention is a Large Language Model-Based AI-powered interactive doll that provides personalized content, education, companionship, entertainment and emotional support to children. The doll connects to a large language model (LLM) and uses a microphone, video camera, touch screen and speaker to interact with the child. The integrated LLM provides real-time video analysis capabilities. The accompanying app provides a full suite of customization options for parents to tailor the doll's interactions to their child's needs and preferences. The doll provides a wide range of content, including educational content, homework assistance, entertainment, AI art, video and music generation, companionship and emotional support, and can suggest activities, tell personalized interactive stories, assisting with language learning, play games, and interact with the child in various other ways. The doll can also be used as a monitor, allowing parents to listen in on the child's interactions, and in another embodiment, to livestream video via an internal camera.

The invention discloses a smart park safety protection system and method based on smart video analysis and multi-mode integration, and relates to the technical field of smart park management. The system comprises a camera, an environment microphone and an access control terminal which are deployed in a park, an edge computing node is used for extracting structured video, audio and access control features, a central server realizes multi-modal space-time alignment through a self-calibration module, a multi-modal fusion module constructs a joint representation vector based on a low-rank tensor algorithm, and the joint representation vector is used for realizing multi-modal space-time alignment. And the security decision module identifies an abnormal event and triggers a response strategy in combination with a classification and anomaly detection model. According to the invention, multi-mode cooperative identification, high-robustness behavior analysis and intelligent response linkage are realized, and the real-time performance, accuracy and intelligent level of a park security system are improved.

A headphone includes a housing including a bottom wall portion and a side wall portion connected with the bottom wall portion which cooperate to form an accommodation space, a sealing ring, a microphone assembly including a sound guiding member configured to hold the sealing ring against a microphone and a support surface provided on a side of the side wall portion facing the accommodation space, and a positioning member inserted between the housing and the sound guiding member to enable the sound guiding member to remain the sealing ring in a press-hold condition. The sealing ring encircling the sound guiding hole provided on the support surface for connecting the accommodation space and an exterior of the housing is provided on a support surface. A sound guiding channel is provided on the sound guiding member for guiding a sound input from the sound guiding hole to the microphone.

A loudspeaker device includes a speaker component, a first microphone element, and a second microphone element. The speaker component includes an earphone core and a core housing. The first microphone element is disposed on a first branch circuit board of a circuit board. The second microphone element is disposed on a second branch circuit board of the circuit board. The circuit board is configured to electrically connect to an audio signal wire, a first auxiliary signal wire, and a second auxiliary signal wire of an external control circuit. The audio signal wire, the first auxiliary signal wire, and the second auxiliary signal wire are electrically and respectively connected to the earphone core, the first microphone element, and the second microphone element through the circuit board. A board surface of the second branch circuit board is tilt with respect to a board surface of the first branch circuit board.

The invention discloses a multi-modal data fusion method and system based on a large-model intelligent agent, belongs to the technical field of artificial intelligence, big data processing and intelligent agents, and aims to solve the technical problem of how to effectively integrate data of different modalities such as texts, images and voices by using the large-model intelligent agent. According to the technical scheme, the method comprises the steps that data collection and preprocessing are conducted, specifically, various modal data of texts, images and voice are collected through a web crawler, an API interface, a camera and microphone equipment, the collected data are preprocessed, the preprocessed multi-modal data are obtained, and the data quality is ensured; feature extraction and mapping: extracting corresponding modal features from the preprocessed multi-modal data through CNN and Transform models, mapping the different modal features to the same space, and combining the aligned features to form comprehensive feature representation; carrying out multi-modal fusion processing; and performing intelligent decision and feedback.

The invention relates to the technical field of audio processing, and discloses an audio processing method and system for speech spectrum reconstruction, and the method comprises the steps: carrying out the preprocessing of an original speech signal collected by a vehicle-mounted microphone, and obtaining a speech signal time-frequency matrix; performing singular value decomposition on the voice signal time-frequency matrix to obtain a sparse matrix representing voice components and a low-rank matrix representing noise components, and establishing a frequency band characteristic incidence matrix through a mutual information entropy calculation method; inputting the sparse matrix into a selection forgetting extreme learning machine for voice feature prediction to obtain predicted voice feature data; and based on the predicted voice feature data and the frequency band feature incidence matrix, performing voice probability calculation and feature interpolation on each time frequency point by adopting a logarithmic frequency spectrum amplitude calculation method to obtain reconstructed frequency spectrum data, thereby improving the accuracy of voice recognition and meeting the requirement of real-time processing in a vehicle-mounted environment at the same time.

Automatic recognition of non-verbal messages in speech, and in particular to detection or analysis of prosodic multilayered analysis of intonation units such as prosodic unit prototypes and their multi-labeled variations, may form a hierarchical classification for the analysis of non¬ verbal information or cues in speech. A speech captured by a microphone is fed to a weakly- supervised deep learning acoustic model for speech recognition and transcription, that may be based on encoder-decoder Transformer architecture, such as Whisper by OpenAI. The model is trained to output multiple words form the text in the captured speech, to identify Intonation Units (IUs) that include one or more words, and associate non-verbal labels to each of the IUs. The labels may indicate a prototype, a discourse function (such as a conversation action), an emotion, an emphasis, or an attitude, as well as a genre of a part of, or whole of, the entire captured speech.

An intelligent (self-learning) sensor-aware and / or context-aware subsystem comprising (i) a System-on-a-Chip (SoC), (ii) a radio transceiver, (iii) a microphone, (iv) a voice processing module, (v) a (bio-inspired) neuromorphic event camera or a hyperspectral camera, (vi) a first set of computer implemental instructions in artificial neural networks (ANN) (which may include a transformer model / diffusion model or Poisson flow generative model++ (PFGM++)), (vii) a second set of computer implementable instructions to analyze and interpret contextual data and (viii) an autonomous artificial intelligence (AI) agent is disclosed.

The invention discloses a speech recognition and authentication method and system based on multi-modal features and dynamic evaluation in the technical field of speech recognition and authentication, and the method comprises the steps: collecting an original speech signal of a user through a microphone, and carrying out the preprocessing of the original speech signal, and obtaining the preprocessing speech data; and extracting feature data of the preprocessed voice data by adopting a multi-dimensional feature hierarchical extraction technology, and injecting a multi-source noise sample into an acoustic feature space of a voiceprint feature model based on an initial training stage established by the voiceprint feature model to construct an anti-noise mixed voiceprint map. Through integrating voiceprint, semantics, behavior characteristics and an environment adaptation mechanism, an authentication threshold is adjusted in real time according to dynamic risk assessment, meanwhile, a risk scoring model is utilized to calculate a comprehensive risk value, authentication modes of different levels are started according to risk scenes of different degrees, and two-factor authentication is forcibly implemented for high-risk scenes. And the authentication security and reliability can be obviously enhanced.

A user electronic device comprising: one or more microphones configured to capture raw audio data; and one or more processors and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: receiving the raw audio data captured by the one or more microphones; processing the raw audio data using a speech transcriber to generate a live transcription of the raw audio data that comprises a plurality of text tokens; processing the raw audio data to generate a speaker identification output that identifies, for each of the text tokens, a respective speaker for each of the text tokens in the live transcription; and processing a first input comprising (i) a first input prompt and (ii) an input text generated from the live transcription using a language model neural network to generate a modified transcription.

The invention discloses a multi-modal self-adaptive child voice interaction method for an educational service robot, relates to the technical field of voice signal processing, and provides a multi-modal self-adaptive child voice interaction method for an educational service robot through multi-array microphone dynamic acquisition and beam forming, self-adaptive noise suppression, multi-speaker separation and speaker identification, and child exclusive model training and online fine tuning. Technical means such as context semantic correction and multi-modal fusion are adopted, so that the target voice focusing and recognition accuracy is greatly improved; multi-person voice tracks are distinguished through an end-to-end network, and rapid distinguishing of a multi-source voice scene is realized in combination with model optimization of child range features; meanwhile, correct vocabularies and pronunciation information are traced back to a speaker library and a child sound model by using real-time semantic correction and interactive feedback, and multi-mode cooperation of a camera and a positioning sensor is used for assisting, so that beams are quickly adjusted and interference is suppressed when a child moves at will and environmental noise suddenly increases, and the sound quality of the child is improved. And natural and smooth voice interaction experience is provided in class and family scenes.

The invention provides a method for eliminating echo of a talkback system, which comprises the following steps of: according to separated near-end speech evaluation and a plurality of recognized speaker speech segments, respectively performing residual echo suppression, constructing binary time-frequency masking and reducing residual echo spectrum caused by dynamic change of echo power; the phase information of the original mixed signal is combined with the enhanced near-end voice power spectrum, the voice waveform of each speaker is reconstructed through inverse short-time Fourier transform, and transition between voice frames is smoothed in an overlap-add mode; whether voice exists at the near end and the far end or not is judged, if the double-talk state is detected, parameters of a nonlinear echo cancellation algorithm are adjusted, and the situation that the voice at the near end is mistakenly suppressed is avoided; and carrying out transmission path modeling on the processed near-end voice signal, and evaluating propagation characteristics of the voice signal from a speaker to a microphone, including time delay and attenuation of direct sound and early reflected sound.

A method of presenting a signal to a speech processing engine is disclosed. According to an example of the method, an audio signal is received via a microphone. A portion of the audio signal is identified, and a probability is determined that the portion comprises speech directed by a user of the speech processing engine as input to the speech processing engine. In accordance with a determination that the probability exceeds a threshold, the portion of the audio signal is presented as input to the speech processing engine. In accordance with a determination that the probability does not exceed the threshold, the portion of the audio signal is not presented as input to the speech processing engine.

The invention discloses an SF6 gas leakage detection method and system based on a photoacoustic spectrum analyzer, and the method comprises the steps: arranging a sampling end to collect an SF6 gas sample, and obtaining stable gas input through constant-current sampling and steady-state pretreatment; steady-state gas is guided into the photoacoustic spectrum analyzer, and resonance frequency stabilization and signal amplification output are achieved through the self-tuning unit; executing double-microphone differential detection and digital filtering processing, and outputting a stable SF6 detection signal with a high signal-to-noise ratio; performing time calibration, abnormity elimination and consistency processing on the detection signal to generate standardized detection data; inputting the standardized data to a Transform model, and performing inversion to generate an SF6 leakage source position and a diffusion path; and displaying an inversion result on a monitoring interface, triggering a sound-light alarm when the inversion result exceeds a limit, and uploading the inversion result to a cloud monitoring platform. According to the invention, the intelligent photoacoustic spectrum system combining photoacoustic-fluid steady-state control and self-tuning detection is constructed, so that high-sensitivity detection and accurate positioning of SF6 gas leakage are realized.

The invention relates to an audio feature extraction method and device based on a neural network. The method comprises the following steps: performing framing processing and fast Fourier transform on an original audio signal acquired by a vehicle-mounted microphone to obtain a frequency domain feature sequence; extracting short-time energy, a zero-crossing rate, a frequency spectrum centroid and a Mel-frequency cepstrum coefficient from the frequency domain feature sequence, and combining first-order difference and second-order difference features of each feature to obtain a multi-dimensional acoustic feature point sequence; performing time sequence arrangement on the multi-dimensional acoustic feature point sequence, constructing an original feature matrix, and performing principal component analysis on the original feature matrix to obtain a feature description matrix; inputting the feature description matrix into a dynamic time feature extraction network for deep time-frequency feature analysis to obtain vehicle-mounted voice deep time-frequency features; and performing voice detection classification based on the vehicle-mounted voice deep time-frequency features, and outputting a vehicle-mounted voice detection result. According to the invention, various noise interferences in a vehicle-mounted environment are effectively suppressed, and the anti-noise capability of the system is enhanced.

Various implementations include audio devices and methods for noise reduction control in wearable audio devices and / or vehicle audio systems. Certain implementations include a non-occluding wearable audio device having: at least one electro-acoustic transducer; at least one microphone; and a control system coupled with the at least one electro-acoustic transducer and the at least one microphone, the control system programmed to: adjust an active noise reduction (ANR) setting for audio output to the at least one electro-acoustic transducer in response to detecting use of the non-occluding wearable audio device in a vehicle.

An artificial intelligence (AI) entry management (EM) security system includes a camera, a microphone, a motion detector, a speaker, and a software platform. Geofences are utilized to create boundaries around physical areas, such as locations for package delivery or property boundaries. The EM device and other devices connected to the software platform are configured to monitor the geofences and detect a presence of an activity, an event, an object, or a device. At least one license plate reader is utilized to read license plate characters. Voice recognition and voice identification are used to grant or deny permissions for individuals on a property

A portable information handling system speaker mount has standardized contacts and mounting points to accept plural speaker types with different speaker characteristics to generate audible sounds driven by an audio subsystem with audio driver settings, such as speaker-type gains. When a speaker is inserted, the audio subsystem communicates a coil characterization signal to the speaker and then analyzes the coil response to identify the type of the speaker, such as by determining the resistance of the coil to a direct current signal or the impedance of the coil from a frequency-based signal. In one example embodiment, the coil characterization signal generates audible noise from the speaker that is detected by a microphone and analyzed to determine the speaker type identity.

A system may include a storage device, configured to store computer-executable instructions. A system may include an ear-bud configured to be positioned within an ear canal of a user, the ear-bud comprising: a speaker, a microphone; and one or more processors in communication with the storage device, wherein the computer-executable instructions, when executed by the one or more processors, cause the one or more processors to: obtain a user hearing profile, obtain an equal-loudness hearing profile, receive audio data from the microphone, and generate a second audio data based on a first sound-pressure level, a second sound-pressure level, a first frequency; and cause the speaker to emit the second audio data within the ear canal of the user, such that the user perceives the audio data as if the user has normal hearing.

An audio cable connector with selectable impedance includes low impedance interface circuitry and high impedance interface circuitry, and a user-operable switch to allow a user to select either of the two impedances to connect to a headset microphone. With the audio cable connector in communication between a headset and equipment, the user may select the appropriate impedance interface circuitry to allow the headset to operate properly with the equipment.

The present invention relates to a computer implemented method for monitoring the blade (210) of a wind turbine (200), the method comprising fixing a sound acquisition device (110) to an outer surface of a tower (220) of the wind turbine (200), wherein the sound acquisition device (110) comprises a microphone, a sound encoder and a communication unit, acquiring a sound sample of sound emitted by the blade (210) during operation of the wind turbine (200), encoding the sound sample into a raw audio file, sending the raw audio file to a remote computing device (120), preprocessing the raw audio file, generating a spectrogram of the pre-processed audio file, detecting an anomaly by applying an image analysis machine learning model to the spectrogram, classifying the anomaly to a damage type by applying a damage type feature analysis machine learning model to frequency domain features of the audio file at the time and frequency of the anomaly, determining a damage location by applying a damage location feature analysis machine learning model to frequency domain features of the audio file at the time and frequency of the, and tracking of a damage severity by extracting audio features of the audio file at the time and frequency of the anomaly indicative of the damage severity depending on the damage type.

The invention discloses a multi-mode speech enhancement system based on audio and video, and the system comprises a data collection module which collects target audio, background noise and interference sound through a microphone; a camera is used for synchronously collecting environment visual information, including facial expressions and mouth shapes of speakers, and noise and interference sounds are normalized to ensure that the noise and the interference sounds are matched with the dynamic range of a target audio; the data analysis module is used for preprocessing the collected audio, including noise suppression and speech enhancement, and improving the speech definition by applying a deep learning model; through the noise reduction and voice enhancement technology, the interference of background noise can be effectively reduced, and the voice is clearer. Thus, audiences can more easily understand speech contents, especially in noisy environments, such as public transport means or busy streets; and the voice recognition accuracy is improved, and the quality of the voice signal can be improved.

The invention relates to a two-parameter calibration-free pulverized coal temperature measurement method based on acoustic tomography, and the method comprises the following steps: 1, arranging a plurality of loudspeaker and microphone combination modules around a measurement field domain for transmitting and receiving acoustic signals, and forming an annular acoustic sensor array; step 2, multi-frequency sound transit time and amplitude attenuation measurement; 3, constructing a tomography inversion problem model of sound velocity and sound attenuation distribution; 4, respectively realizing tomography inversion of sound velocity and sound attenuation distribution through an improved joint iterative reconstruction method SIRT, and solving pulverized coal accumulation slowness and sound attenuation coefficient distribution under each frequency; 5, calculating a corresponding multi-frequency complex wave number through the multi-frequency slowness and the sound attenuation coefficient in each grid; step 6, constructing a temperature decoupling model of a complex wave number; and 7, regarding the temperature decoupling model of the complex wave number as a nonlinear objective function optimization problem, establishing a nonlinear objective function, and performing parameter decoupling and temperature calculation.

The invention discloses a personalized digital human image creation and generation system based on deep learning, which relates to the technical field of digital human generation and comprises a multivariate data acquisition module, an image creation module, a model storage module, a matching module and a rendering interaction module. The multivariate data acquisition module collects facial images and whole body photos through a camera, and records voice samples through a microphone. According to the invention, by constructing the multivariate data acquisition module, the image creation module, the model storage module, the matching module and the rendering interaction module, full-process automatic processing from data acquisition to digital human image generation is realized; the multivariate data acquisition module can comprehensively capture the appearance and voice features of a user, and the image creation module extracts feature vectors by using a deep learning technology and generates a personalized digital human image by using a generative adversarial network technology.

The invention relates to the technical field of audio signal processing, and discloses a room sound correction method, device and equipment for audio equipment and a storage medium, which are used for improving the accuracy and correction effect of correction parameters and effectively improving the acoustic effect of the audio equipment in a room. The room sound correction method of the audio equipment comprises the following steps: acquiring response data of a room to a multi-band preset test signal, and extracting room acoustic data by adopting a time-frequency conjoint analysis method, the room acoustic data comprising frequency attenuation, reflection paths, phase shifts and reverberation time information of different bands; based on the room acoustic data, performing compensation processing by using preset microphone calibration data to obtain a calibrated frequency response curve; performing fitting calculation on a preset ideal frequency curve and the calibrated frequency response curve according to an optimization algorithm to obtain specific correction parameters of the room; and correspondingly adjusting the output of the audio equipment according to the correction parameter.

A logarithmic amplifier includes programmable gain amplifiers each having a different gain, wherein an input of each of the programmable gain amplifiers is coupled to an input of the logarithmic amplifier; and a summing circuit having inputs coupled to a corresponding output of each of the programmable gain amplifiers and an output coupled to an output of the logarithmic amplifier, wherein the summing circuit generates a logarithmic transfer function having piecewise linear segments.

Various embodiments of an ear-wearable electronic device are disclosed. The device includes a housing having a top shell and a bottom shell, a microphone disposed within the housing, and an acoustic path disposed at least partially within the housing and extending between an acoustic port disposed at an outer surface of the housing and an outlet disposed within the housing that acoustically couples the acoustic path to an inlet of the microphone. The device further includes an acoustic filter disposed at least partially within an inner surface of the top shell and includes a neck and a resonance cavity acoustically coupled to the neck. The acoustic filter is acoustically coupled to the acoustic path via the neck. Further, the acoustic filter is configured to reduce an intensity of acoustic waves sensed by the microphone in a first frequency range.