11664 results about "Speech recognition" patented technology

An example assistant system can use a multimodal multitask medical machine-learned model to perform image processing to answer natural language queries. A device can process speech data or other natural language inputs to obtain a query. The query can be processed alongside image data that provides context for the query. The example system can receive a query associated with a particular task domain; generate, based on the query, a query input that comprises query instruction data from a first modality and query context data from a second modality; generate a combined input comprising the query input and an exemplar input, wherein the exemplar input comprises exemplar instruction data from the first modality and an exemplar context placeholder in lieu of exemplar context data from the second modality; process the combined input with a multimodal machine-learned model to generate output data; and output a query response based on the output data.

The invention relates to the technical field of structural health monitoring and intelligent diagnosis, in particular to a structural fatigue damage identification method based on acoustic emission and deep learning, and the method comprises the steps: collecting a structural response signal under a fatigue load through an acoustic emission sensor array, inputting the structural response signal to a CNN-BiLSTM-Attention mixed deep learning model, and carrying out the recognition of the structural fatigue damage through the CNN-BiLSTM-Attention mixed deep learning model; the model extracts local time domain features through a dynamic adaptive convolution kernel, captures long time sequence dependence by using a bidirectional long-short-term memory network, focuses key damage features through a bimodal space-time attention mechanism, divides damage stages based on a nonlinear dynamic threshold algorithm of fracture opening amount, constructs a training data set of physical-data fusion, and performs dynamic time domain feature extraction. The learning rate is optimized by adopting a gradient sensitive cosine annealing algorithm, and the robustness of the model is improved in combination with an anti-noise and anti-loss function. The method integrates physical characteristics and an intelligent algorithm, and has the advantages of adaptive noise suppression, strong cross-domain generalization ability, high real-time performance and the like.

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

Techniques for managing access to sensitive data in transcriptions are described. A method for managing access to sensitive data in transcriptions may include receiving a request to generate a redacted transcript of content, obtaining a transcript of the content, sending at least a portion of the transcript to a model endpoint to identify sensitive entities in the transcript, receiving an inference response identifying one or more sensitive entities in the transcript, and generating the redacted transcript based at least one the transcript and the inference response.

According to an aspect of the present invention, there is provided an electrical audio signal processing system and device, comprising: a computer graphics processing and selective visual display system with a screen; an eye tracking device; a processor; one or more computer memory devices; wherein the processor is arranged for operations comprising: measuring the user's eye movements to ascertain the specific word on which the user is fixated, by the eye tracking device; modifying the display at the user's current fixation point; applying a delay between the presentation of successive graphic elements based on the user's calculated rate to accommodate the user's required time; and presenting elements to the user at a rate based upon the user's required time.

The invention relates to an audio noise reduction method, device and system based on deep learning, and the method comprises the steps: obtaining an input audio signal with noise, and carrying out the multi-scale time-frequency decomposition, and obtaining a mixed time-frequency feature and a noise fingerprint spectrum; performing parameter parallel processing on the noise fingerprint spectrum through a preset dynamic kernel generation network, and performing preliminary noise reduction processing on the mixed time-frequency characteristics to obtain noise-reduced mixed data; performing dual-path processing structure construction on the noise reduction mixed data to obtain amplitude optimization data and phase optimization data; performing dynamic time-frequency domain cross fusion on the amplitude optimization data and the phase optimization data to obtain fused audio data; and carrying out differentiable acoustic equation constraint adversarial training on the fused audio data, and carrying out inverse time-frequency transformation processing to obtain a target noise-reduced audio signal. According to the invention, the overall efficiency and effect of audio signal processing can be effectively improved.

Methods and apparatus are described for evaluating dubbing of media content. Emotions are identified based on combinations of attributes determined for segments of a source language audio and a dubbed audio. The emotions may be compared to determine emotional prosody transfer between the source audio and dubbed audio. Based on the comparison, a notification is generated indicating whether an emotion classification associated with the source audio matches an emotion classification associated with the dubbed audio.

The invention relates to the technical field of audio testing, and discloses an intelligent audio testing system frequency response analysis method, device and equipment, and the method comprises the steps: carrying out the audio parameter initialization of an intelligent audio testing system, and obtaining a testing parameter matrix and an excitation signal; performing dual-channel acquisition and time-frequency feature extraction to obtain a time-frequency feature data set; executing double-branch processing of time domain noise reduction and frequency domain enhancement to obtain a purified feature data set and a feature weighting matrix; performing segmented recursive calculation and feature aggregation to obtain a frequency response feature vector; decomposing the frequency response feature vector according to a test frequency band, and performing environment compensation and nonlinear calibration to obtain a standard frequency response data set; and inputting the standard frequency response data set into the deep neural network for feature recognition and mode classification to obtain a frequency response curve and a phase characteristic curve, thereby improving the test precision, the anti-interference capability, the automation degree and the adaptability of the intelligent audio test system.

The subject technology provides frequency-aware masked autoencoders for multimodal pretraining on frequency-based signals. An apparatus receives input data comprising frequency-based signal information associated with one or more modalities. The apparatus transforms the input data from a time domain to a frequency domain. The apparatus generates a frequency-embedded latent representation of the input data comprising time-domain and frequency-domain information. The apparatus also generates a masked frequency-embedded latent representation by masking one or more frequency components in the frequency-embedded latent representation. The apparatus produces a trained machine learning model by training a neural network to predict one or more masked frequency components of the frequency-embedded latent representation.

Automatic video dubbing methods and apparatuses automatically dub a video signal into a different target language than the original while controllably preserving vocal characteristics of original speakers in the corresponding audio, based on separating audio from the video, preserving audio timings, separating voice and non-voice segments from background noise, and using ASR, MT and signal processing techniques to generate properly sized and selectable speech translations into different languages corresponding to speaker voices in the video and emotions, voice and / or textual characteristics.

The invention provides a real-time video translation and audio and picture synchronization method and system based on a multi-modal large model, and relates to the technical field of video translations, and the method comprises the steps: obtaining a source video; extracting the source video based on the multi-modal large model to obtain a multi-modal feature; fusing the multi-modal features through a cross-modal attention mechanism to generate a context semantic vector; translating into a target language text in real time based on the context semantic vector, and processing the translated language text based on the multi-modal features to obtain a translated language sound source; and performing mouth shape adjustment on the source video based on the translation language sound source, and merging the translation language sound source and the mouth shape animation video to obtain a real-time translation video with synchronous sound and picture. According to the method, the limitation of traditional single-modal translation is broken through, and the semantic accuracy of translation is remarkably improved by dynamically aligning the context information through the multi-modal features in combination with a cross-modal attention mechanism.

A metaverse application receives a user-provided audio stream associated with a user. The metaverse application obtains portions of one or more audio streams. The metaverse application divides the user-provided audio stream into a plurality of portions, wherein each portion corresponds to a particular time window of the audio stream. The metaverse application providing the plurality of portions of the user-provided audio stream as input to an audio machine-learning model. The audio machine-learning model outputs, based on the portions of the user-provided audio stream, a determination of abuse in a particular portion of the plurality of portions. The metaverse application performs a remedial action responsive to the determination of abuse in the particular portion.

Disclosed are an audio parameter adjustment method, an audio parameter adjustment apparatus, an audio parameter adjustment device and a storage medium. The method includes: obtaining a target audio effect of an audio system, and analyzing the target audio effect to obtain a corresponding audio metric parameter; determining a target environment where the audio system is currently located, and calculating an environmental optimization parameter of the target environment; calculating, based on the audio metric parameter and the environmental optimization parameter, an optimization parameter of the audio system; and performing adaptive adjustment on the audio system based on the optimization parameter of the audio system.

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 hearing system comprises a hearing device, e.g. a hearing aid or a headset, configured to be worn by a user. The hearing device comprises at least one input transducer for providing at least one electric input signal representative of sound in the environment of the hearing device, wherein said at least one electric input signal comprises a target signal component assumed to be of current interest to the user, and a noise component. The hearing device further comprises a noise control system configured to provide an estimate of said target signal component and an estimate of said noise component and to apply a statistical structure to said noise component to thereby provide a modified noise component comprising said statistical structure; and to determine a modified estimate of said target signal component in dependence of said modified noise component. Thereby an improved segregation of sound sources may be provided.

The invention discloses a method for realizing synchronization of a character expression and a lip shape in a video through an emotion in sound and a cloned digital human system, and the method for realizing synchronization of the character expression and the lip shape in the video through the emotion in the sound comprises the steps: collecting audio information, and extracting multi-dimensional features of the sound; applying a pre-trained expression and lip shape generation model to generate corresponding expression parameters and lip shape parameters according to the multi-dimensional features of the sound; performing fusion processing on the expression parameters and the lip shape parameters, and generating a continuous animation sequence according to the fused parameters; and rendering the continuous animation sequence to generate video information with expressions, lip shapes and sound emotion synchronization. The high-precision expression and lip shape synchronization is realized, the natural fidelity of the cloned digital human is improved, and the application range and value of the cloned digital human are expanded.

The invention provides a dance video generation method based on multi-mode music driving and frequency domain-space double-flow decomposition, and the method comprises the steps: extracting multi-granularity music features through a composite encoder (Librosa + Jukebox), employing a beat gating attention mechanism, enabling the key actions such as dancing hand raising, kicking and the like to be strictly aligned with a music re-beat point, and enabling the synchronization error to be reduced to 118 ms through the verification of a test data set; for the problem of visual detail loss, a frequency domain-space double-flow decomposition architecture is provided, a Butterworth filter bank is used to decouple a reference image into a low-frequency energy diagram and a high-frequency residual error, and a double-flow diffusion mechanism is used to optimize a global attitude and local details respectively; a joint confidence prediction module is introduced for the generation stability in a shielding scene, and the motion trail of an abnormal joint point is dynamically corrected through a time domain sliding window weighted fusion strategy, so that a reasonable action conforming to ergonomics can still be generated under a 50% limb shielding rate.

Automated attention handling techniques are described herein for use with wearable audio components with active noise control (ANC) to suppress ambient sound. A name embedding model is trained automatically to convert name audio samples into acoustic segments based on a knowledge distillation model. The name embedding model is used to generate reference embeddings for each of a user-enrolled set of names, and a relation network and a false rejection network are also trained. In real-time operation, the name embedding model converts real-time audio samples to real-time embeddings, the relation network compared the real-time embeddings to the reference embeddings to look for candidate matches, and the false rejection network validates the candidate matches to detect when one of the user-enrolled names has been invoked. Detecting such an invocation automatically triggers the ANC to switch to a conversation mode.

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.

Apparatuses, systems, and techniques to selectively suppress noise in a conference call. In at least one embodiment, one or more neural networks may be used to identify a source of a noise component and cause a notification to be sent, informing a user of the noise source to confirm or decline suppression of the noise component.

Automated attention handling techniques are described herein for use with wearable audio components with active noise control (ANC) to suppress ambient sound. A name embedding model is trained automatically to convert name audio samples into linguistically distinct name classifications and / or unified audio samples. The name embedding model is used to generate reference embeddings for each of a user-enrolled set of names, and a relation network and a false rejection network are also trained. In real-time operation, the name embedding model converts real-time audio samples to real-time embeddings, the relation network compared the real-time embeddings to the reference embeddings to look for candidate matches, and the false rejection network validates the candidate matches to detect when one of the user-enrolled names has been invoked. Detecting such an invocation automatically triggers the ANC to switch to a conversation mode.

A method may include obtaining a first video data including sign language originating at a first device during a communication session, obtaining one or more features from the first video data, and determining one or more matching functions from the one or more features. The method may further include determining, using a language model, a first set of one or more symbols from the one or more matching functions, and determining a second set of one or more symbols from the first set of one or more symbols.

The invention provides an audio watermark generation method, audio watermark generation equipment and a computer storage medium. The audio watermark generation method comprises the following steps: acquiring target audio data; extracting amplitude spectrum features and phase spectrum features of the target audio data; acquiring watermark adding frame information of the amplitude spectrum features; selecting one watermark information code from a watermark codebook library, and performing frame-level copying according to the frame number of the amplitude spectrum characteristics to obtain first watermark data; performing frame selection on the first watermark data according to the watermark adding frame information to obtain second watermark data; fusing the amplitude spectrum feature with the second watermark data to obtain a watermark amplitude spectrum feature; and fusing the watermark amplitude spectrum feature and the phase spectrum feature to obtain watermark audio data. According to the audio watermark generation method, the watermark adding selector is designed, the frame number and the frame number needing to be added with the watermark are selected through the frame selection logic, and it is guaranteed that after the audio watermark is generated, the audio listening feeling is not affected.

An audio encoder has a first information sink oriented encoding branch such as a spectral domain encoding branch, a second information source or SNR oriented encoding branch such as an LPC-domain encoding branch, and a switch for switching between the first and second encoding branches, the second encoding branch having a converter into a specific domain different from the spectral domain such as an LPC analysis stage generating an excitation signal, and the second encoding branch having a specific domain coding branch such as LPC domain processing branch, and a specific spectral domain coding branch such as LPC spectral domain processing branch, and an additional switch for switching between the specific domain coding branch and the specific spectral domain coding branch. An audio decoder has a first domain decoder, a second domain decoder, and a third domain decoder as well as two cascaded switches for switching between the decoders.

The invention relates to the technical field of electroencephalogram signal processing, in particular to an ontology brain wave audio auditory perception synchronous feedback method, device and system and electronic equipment. The method comprises the following steps: receiving electroencephalogram signals of a collected user on line from electroencephalogram collection equipment through an upper computer, obtaining electroencephalogram signals of a specified frequency band from the electroencephalogram signals, and extracting corresponding electroencephalogram characteristics; according to the electroencephalogram features or preset rhythm parameters, the electroencephalogram signals of the specified frequency band are segmented into a plurality of electroencephalogram segments, a plurality of audio expressions corresponding to the electroencephalogram segments are generated, and feature parameters of the audio expressions are determined according to the electroencephalogram features of the corresponding electroencephalogram segments; generating brain wave audio representation data according to the audio representation corresponding to the electroencephalogram signals of the one or more designated frequency bands, and obtaining brain wave audio according to the brain wave audio representation data. Therefore, the physiological suitability of nerve regulation and control and the artistic expressivity of audio generation are met at the same time, and organic unification of nerve regulation and control and audio generation is achieved.

Automated attention handling techniques are described herein for use with wearable audio components with active noise control (ANC) to suppress ambient sound. A name embedding model is trained automatically to convert name audio samples into linguistically distinct name classifications and / or unified audio samples. The name embedding model is used to generate reference embeddings for each of a user-enrolled set of names, and a relation network and a false rejection network are also trained. In real-time operation, the name embedding model converts real-time audio samples to real-time embeddings, the relation network compared the real-time embeddings to the reference embeddings to look for candidate matches, and the false rejection network validates the candidate matches to detect when one of the user-enrolled names has been invoked. Detecting such an invocation automatically triggers the ANC to switch to a conversation mode.

The invention relates to the technical field of audio noise reduction, in particular to a hybrid noise reduction method and system for a Bluetooth headset. The method comprises the following steps: obtaining multi-dimensional environment noise data based on an environment noise sensor, and carrying out noise component feature analysis to generate environment noise multi-frequency component features; performing intelligent noise reduction intensity and self-adaptive filtering noise reduction suppression on the multi-frequency component characteristics of the environmental noise so as to generate an external environmental noise reduction suppression strategy; a micro audio signal is sent based on an ear canal form sensor, and audio signal feedback information is collected; performing three-dimensional ear canal structure modeling according to the audio signal feedback information so as to construct a three-dimensional ear canal structure model of the user; and obtaining a noise reduction feedback log of the wearer, and carrying out personalized noise reduction demand analysis to obtain the noise reduction demand of the user in the current scene. According to the invention, through comprehensive and finer mixed noise suppression, the audio quality and stability of the Bluetooth earphone are improved.

A computer system for upsampling decompressed audio data after lossy compression using specialized neural network techniques. The system processes compressed audio channels through an audio pre-processor that extracts spectral information, detects speech activity, segments audio, and normalizes input levels. A trained deep learning algorithm with multi-channel transformers using channel-wise and self-attention mechanisms recovers information lost during compression. The system further enhances audio quality through a time-frequency domain transformer applying Fourier transforms and Mel-scale frequency processing, while a perceptual quality assessor employing psychoacoustic models evaluates the output. This specialized audio processing approach significantly improves reconstructed audio quality by leveraging correlations between audio channels, addressing both spectral and temporal features, and optimizing for human perception characteristics, resulting in higher fidelity audio reproduction from compressed formats.

Systems and methods for converting music into segmented digital assets for dynamic uses in digital experiences and discovery thereto are provided. In one example, a digital music file having at least one song is processed to identify segmented portions of the song, which are separated. The segmented portions of the song are analyzed to identify at least one musical quality of each of the segmented portions. An emotion, style, or vibe attribute is correlated to each of the segmented portions based on the musical quality identified. A composite soundtrack is constructed from the segmented portions by arranging at least a portion of the segmented portions based on the emotion, style, or vibe attribute correlated with each of the segmented portions. The composite soundtrack may be used within a digital entertainment experience.